Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells69567
Missing cells (%)49.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory131.0 B

Variable types

Text1
Numeric9
Categorical2
Unsupported2

Dataset

Description한국세라믹기술원 세라믹소재정보은행의 기초 물성 정보입니다.
Author한국세라믹기술원
URLhttps://www.data.go.kr/data/15072096/fileData.do

Alerts

생성자 is highly overall correlated with 비열 and 6 other fieldsHigh correlation
생성일자 is highly overall correlated with 비열 and 5 other fieldsHigh correlation
비열 is highly overall correlated with 기브스 에너지 and 2 other fieldsHigh correlation
엔탈피 is highly overall correlated with 기브스 에너지 and 2 other fieldsHigh correlation
기브스 에너지 is highly overall correlated with 비열 and 3 other fieldsHigh correlation
녹는점 is highly overall correlated with 끓는점 and 3 other fieldsHigh correlation
끓는점 is highly overall correlated with 녹는점 and 3 other fieldsHigh correlation
용해열 is highly overall correlated with 녹는점 and 3 other fieldsHigh correlation
기화열 is highly overall correlated with 녹는점 and 2 other fieldsHigh correlation
비중 is highly overall correlated with 생성자 and 1 other fieldsHigh correlation
생성자 is highly imbalanced (97.4%)Imbalance
생성일자 is highly imbalanced (95.9%)Imbalance
녹는점 has 9943 (99.4%) missing valuesMissing
끓는점 has 9893 (98.9%) missing valuesMissing
용해열 has 9943 (99.4%) missing valuesMissing
기화열 has 9900 (99.0%) missing valuesMissing
비중 has 9885 (98.9%) missing valuesMissing
수정자 has 10000 (100.0%) missing valuesMissing
수정일자 has 10000 (100.0%) missing valuesMissing
수정자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
수정일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
온도 has 133 (1.3%) zerosZeros

Reproduction

Analysis started2023-12-12 21:14:27.144245
Analysis finished2023-12-12 21:14:37.411666
Duration10.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재시퀀스
Text

Distinct440
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T06:14:37.663471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st rowB100206
2nd rowB100212
3rd rowB100219
4th rowB100255
5th rowB100809
ValueCountFrequency (%)
b100319 51
 
0.5%
b100880 51
 
0.5%
b100120 50
 
0.5%
b100622 49
 
0.5%
b100104 48
 
0.5%
b100255 48
 
0.5%
b100105 48
 
0.5%
b100537 47
 
0.5%
b100107 46
 
0.5%
b100703 45
 
0.4%
Other values (430) 9517
95.2%
2023-12-13T06:14:38.153627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23063
32.9%
1 14495
20.7%
B 10000
14.3%
2 4179
 
6.0%
8 3462
 
4.9%
6 3332
 
4.8%
3 2901
 
4.1%
7 2542
 
3.6%
5 2485
 
3.5%
9 2151
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
85.7%
Uppercase Letter 10000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23063
38.4%
1 14495
24.2%
2 4179
 
7.0%
8 3462
 
5.8%
6 3332
 
5.6%
3 2901
 
4.8%
7 2542
 
4.2%
5 2485
 
4.1%
9 2151
 
3.6%
4 1390
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
B 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
85.7%
Latin 10000
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23063
38.4%
1 14495
24.2%
2 4179
 
7.0%
8 3462
 
5.8%
6 3332
 
5.6%
3 2901
 
4.8%
7 2542
 
4.2%
5 2485
 
4.1%
9 2151
 
3.6%
4 1390
 
2.3%
Latin
ValueCountFrequency (%)
B 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23063
32.9%
1 14495
20.7%
B 10000
14.3%
2 4179
 
6.0%
8 3462
 
4.9%
6 3332
 
4.8%
3 2901
 
4.1%
7 2542
 
3.6%
5 2485
 
3.5%
9 2151
 
3.1%

온도
Real number (ℝ)

ZEROS 

Distinct132
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean847.516
Minimum0
Maximum2880
Zeros133
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:38.317764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q1360
median760
Q31300
95-th percentile1860
Maximum2880
Range2880
Interquartile range (IQR)940

Descriptive statistics

Standard deviation568.93595
Coefficient of variation (CV)0.67129819
Kurtosis-0.89062959
Mean847.516
Median Absolute Deviation (MAD)460
Skewness0.38155216
Sum8475160
Variance323688.12
MonotonicityNot monotonic
2023-12-13T06:14:38.528558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 147
 
1.5%
600 147
 
1.5%
200 145
 
1.5%
220 144
 
1.4%
60 142
 
1.4%
280 141
 
1.4%
360 141
 
1.4%
400 141
 
1.4%
340 141
 
1.4%
40 140
 
1.4%
Other values (122) 8571
85.7%
ValueCountFrequency (%)
0 133
1.3%
20 139
1.4%
40 140
1.4%
60 142
1.4%
80 137
1.4%
100 147
1.5%
120 130
1.3%
140 118
1.2%
160 131
1.3%
180 131
1.3%
ValueCountFrequency (%)
2880 1
< 0.1%
2780 1
< 0.1%
2720 1
< 0.1%
2680 1
< 0.1%
2660 1
< 0.1%
2640 1
< 0.1%
2620 1
< 0.1%
2580 2
< 0.1%
2520 2
< 0.1%
2500 1
< 0.1%

비열
Real number (ℝ)

HIGH CORRELATION 

Distinct8443
Distinct (%)84.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean126.62561
Minimum1
Maximum1549.2546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:38.671087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48.330789
Q160.843496
median87.379094
Q3132.51106
95-th percentile384.53801
Maximum1549.2546
Range1548.2546
Interquartile range (IQR)71.667564

Descriptive statistics

Standard deviation137.32851
Coefficient of variation (CV)1.084524
Kurtosis37.489602
Mean126.62561
Median Absolute Deviation (MAD)31.566834
Skewness5.1261578
Sum1266129.5
Variance18859.12
MonotonicityNot monotonic
2023-12-13T06:14:38.817177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.152 30
 
0.3%
123.0096 23
 
0.2%
171.9624 20
 
0.2%
120.4992 18
 
0.2%
123.8464 18
 
0.2%
117.9888 18
 
0.2%
123.84641 17
 
0.2%
115.00868 16
 
0.2%
121.336 15
 
0.1%
170.7072 15
 
0.1%
Other values (8433) 9809
98.1%
ValueCountFrequency (%)
1.0 1
< 0.1%
28.155141 1
< 0.1%
30.983107 1
< 0.1%
31.196881 1
< 0.1%
31.48093 1
< 0.1%
31.715319 1
< 0.1%
32.508353 1
< 0.1%
33.239689 1
< 0.1%
33.3402 1
< 0.1%
33.704106 1
< 0.1%
ValueCountFrequency (%)
1549.2546 2
< 0.1%
1549.2544 1
< 0.1%
1546.4452 1
< 0.1%
1545.2989 1
< 0.1%
1544.0956 1
< 0.1%
1543.4715 1
< 0.1%
1541.497 1
< 0.1%
1537.0383 1
< 0.1%
1535.3759 1
< 0.1%
1533.6111 1
< 0.1%

엔탈피
Real number (ℝ)

HIGH CORRELATION 

Distinct9939
Distinct (%)99.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-724208.91
Minimum-18446167
Maximum967554.53
Zeros0
Zeros (%)0.0%
Negative9301
Negative (%)93.0%
Memory size166.0 KiB
2023-12-13T06:14:38.974890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-18446167
5-th percentile-1846627.2
Q1-888215.99
median-328705.45
Q3-123004.64
95-th percentile24462.433
Maximum967554.53
Range19413722
Interquartile range (IQR)765211.36

Descriptive statistics

Standard deviation1463661.6
Coefficient of variation (CV)-2.0210488
Kurtosis59.723774
Mean-724208.91
Median Absolute Deviation (MAD)258612.99
Skewness-6.596509
Sum-7.2413649 × 109
Variance2.1423052 × 1012
MonotonicityNot monotonic
2023-12-13T06:14:39.128284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-56374.375 3
 
< 0.1%
-133099.8 3
 
< 0.1%
-90030.379 3
 
< 0.1%
-121909.47 3
 
< 0.1%
-67308.947 3
 
< 0.1%
-134789.13 3
 
< 0.1%
-28081.694 2
 
< 0.1%
9698.9588 2
 
< 0.1%
-43397.485 2
 
< 0.1%
10915.829 2
 
< 0.1%
Other values (9929) 9973
99.7%
ValueCountFrequency (%)
-18446167.0 1
< 0.1%
-18421574.0 1
< 0.1%
-18370996.0 1
< 0.1%
-18265457.0 1
< 0.1%
-18155675.0 1
< 0.1%
-17985666.0 1
< 0.1%
-17898940.0 1
< 0.1%
-17869830.0 1
< 0.1%
-17840631.0 1
< 0.1%
-17752541.0 1
< 0.1%
ValueCountFrequency (%)
967554.53 1
< 0.1%
925246.64 1
< 0.1%
841884.6 1
< 0.1%
821303.86 1
< 0.1%
680683.65 1
< 0.1%
671358.63 1
< 0.1%
643383.54 1
< 0.1%
624733.49 1
< 0.1%
621135.55 1
< 0.1%
606083.43 1
< 0.1%

기브스 에너지
Real number (ℝ)

HIGH CORRELATION 

Distinct9961
Distinct (%)99.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-1039926
Minimum-23854884
Maximum3
Zeros0
Zeros (%)0.0%
Negative9998
Negative (%)> 99.9%
Memory size166.0 KiB
2023-12-13T06:14:39.270229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23854884
5-th percentile-2476009.9
Q1-1219229.7
median-560125.15
Q3-313245.12
95-th percentile-140888.07
Maximum3
Range23854887
Interquartile range (IQR)905984.57

Descriptive statistics

Standard deviation1768310.5
Coefficient of variation (CV)-1.7004195
Kurtosis62.464581
Mean-1039926
Median Absolute Deviation (MAD)308381.8
Skewness-6.7399111
Sum-1.0398221 × 1010
Variance3.126922 × 1012
MonotonicityNot monotonic
2023-12-13T06:14:39.409725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-251715.28 3
 
< 0.1%
-152210.82 3
 
< 0.1%
-270526.27 3
 
< 0.1%
-150526.99 3
 
< 0.1%
-213356.16 3
 
< 0.1%
-165375.52 3
 
< 0.1%
-179673.52 2
 
< 0.1%
-191173.32 2
 
< 0.1%
-247175.9 2
 
< 0.1%
-282799.75 2
 
< 0.1%
Other values (9951) 9973
99.7%
ValueCountFrequency (%)
-23854884.0 1
< 0.1%
-23779367.0 1
< 0.1%
-23480287.0 1
< 0.1%
-23040966.0 1
< 0.1%
-22897105.0 1
< 0.1%
-22754578.0 1
< 0.1%
-22683824.0 1
< 0.1%
-22473643.0 1
< 0.1%
-22062998.0 1
< 0.1%
-21929119.0 1
< 0.1%
ValueCountFrequency (%)
3.0 1
< 0.1%
-37636.994 1
< 0.1%
-39842.62 1
< 0.1%
-46795.032 1
< 0.1%
-50332.74 1
< 0.1%
-51287.793 1
< 0.1%
-51812.602 1
< 0.1%
-53733.785 1
< 0.1%
-54777.72 1
< 0.1%
-57499.252 1
< 0.1%

녹는점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)100.0%
Missing9943
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean1422.3684
Minimum4
Maximum4000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:39.560283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile175.2
Q1670
median1244
Q32013
95-th percentile3306.4
Maximum4000
Range3996
Interquartile range (IQR)1343

Descriptive statistics

Standard deviation932.30424
Coefficient of variation (CV)0.65545904
Kurtosis0.091825978
Mean1422.3684
Median Absolute Deviation (MAD)656
Skewness0.74102205
Sum81075
Variance869191.2
MonotonicityNot monotonic
2023-12-13T06:14:39.737341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3532 1
 
< 0.1%
4000 1
 
< 0.1%
2310 1
 
< 0.1%
1447 1
 
< 0.1%
822 1
 
< 0.1%
191 1
 
< 0.1%
1922 1
 
< 0.1%
1437 1
 
< 0.1%
2330 1
 
< 0.1%
2067 1
 
< 0.1%
Other values (47) 47
 
0.5%
(Missing) 9943
99.4%
ValueCountFrequency (%)
4 1
< 0.1%
95 1
< 0.1%
112 1
< 0.1%
191 1
< 0.1%
213 1
< 0.1%
365 1
< 0.1%
480 1
< 0.1%
558 1
< 0.1%
587 1
< 0.1%
588 1
< 0.1%
ValueCountFrequency (%)
4000 1
< 0.1%
3532 1
< 0.1%
3396 1
< 0.1%
3284 1
< 0.1%
2825 1
< 0.1%
2689 1
< 0.1%
2515 1
< 0.1%
2403 1
< 0.1%
2330 1
< 0.1%
2310 1
< 0.1%

끓는점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct104
Distinct (%)97.2%
Missing9893
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean2259.3551
Minimum5
Maximum6988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:40.185005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile381
Q11243.5
median2180
Q33149.5
95-th percentile4341.2
Maximum6988
Range6983
Interquartile range (IQR)1906

Descriptive statistics

Standard deviation1310.9879
Coefficient of variation (CV)0.58024872
Kurtosis0.35176096
Mean2259.3551
Median Absolute Deviation (MAD)969
Skewness0.58388118
Sum241751
Variance1718689.3
MonotonicityNot monotonic
2023-12-13T06:14:40.328293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1592 2
 
< 0.1%
1652 2
 
< 0.1%
713 2
 
< 0.1%
4212 1
 
< 0.1%
1400 1
 
< 0.1%
2923 1
 
< 0.1%
3980 1
 
< 0.1%
5 1
 
< 0.1%
3299 1
 
< 0.1%
3058 1
 
< 0.1%
Other values (94) 94
 
0.9%
(Missing) 9893
98.9%
ValueCountFrequency (%)
5 1
< 0.1%
191 1
< 0.1%
229 1
< 0.1%
284 1
< 0.1%
322 1
< 0.1%
375 1
< 0.1%
395 1
< 0.1%
468 1
< 0.1%
486 1
< 0.1%
527 1
< 0.1%
ValueCountFrequency (%)
6988 1
< 0.1%
5075 1
< 0.1%
4823 1
< 0.1%
4777 1
< 0.1%
4639 1
< 0.1%
4343 1
< 0.1%
4337 1
< 0.1%
4305 1
< 0.1%
4212 1
< 0.1%
3980 1
< 0.1%

용해열
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)100.0%
Missing9943
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean82046.198
Minimum6
Maximum867110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:40.469892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9581.2
Q122929
median48534
Q364850
95-th percentile170808.4
Maximum867110
Range867104
Interquartile range (IQR)41921

Descriptive statistics

Standard deviation152089.22
Coefficient of variation (CV)1.8537022
Kurtosis20.731763
Mean82046.198
Median Absolute Deviation (MAD)25522
Skewness4.4956406
Sum4676633.3
Variance2.313113 × 1010
MonotonicityNot monotonic
2023-12-13T06:14:40.627418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79496.24 1
 
< 0.1%
104600.2 1
 
< 0.1%
64850.0 1
 
< 0.1%
50473.0 1
 
< 0.1%
64775.0 1
 
< 0.1%
15900.0 1
 
< 0.1%
47480.0 1
 
< 0.1%
55650.0 1
 
< 0.1%
129700.0 1
 
< 0.1%
117521.0 1
 
< 0.1%
Other values (47) 47
 
0.5%
(Missing) 9943
99.4%
ValueCountFrequency (%)
6.0 1
< 0.1%
7866.1 1
< 0.1%
9414.0 1
< 0.1%
9623.0 1
< 0.1%
10878.5 1
< 0.1%
10878.55 1
< 0.1%
15900.0 1
< 0.1%
17200.0 1
< 0.1%
18830.0 1
< 0.1%
19748.2 1
< 0.1%
ValueCountFrequency (%)
867110.0 1
< 0.1%
796219.0 1
< 0.1%
314002.0 1
< 0.1%
135010.0 1
< 0.1%
129700.0 1
< 0.1%
117521.0 1
< 0.1%
107832.0 1
< 0.1%
105000.0 1
< 0.1%
104600.2 1
< 0.1%
104270.0 1
< 0.1%

기화열
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)100.0%
Missing9900
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean674709.91
Minimum29657.4
Maximum8276319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:40.813224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29657.4
5-th percentile43842.25
Q1109214.8
median321800.25
Q3717512.25
95-th percentile1921422.6
Maximum8276319
Range8246661.6
Interquartile range (IQR)608297.45

Descriptive statistics

Standard deviation1118537.1
Coefficient of variation (CV)1.6578045
Kurtosis24.158622
Mean674709.91
Median Absolute Deviation (MAD)230702.5
Skewness4.3680381
Sum67470991
Variance1.2511253 × 1012
MonotonicityNot monotonic
2023-12-13T06:14:40.970286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78271.0 1
 
< 0.1%
88186.3 1
 
< 0.1%
1262442.0 1
 
< 0.1%
436456.2 1
 
< 0.1%
1144744.0 1
 
< 0.1%
1073770.6 1
 
< 0.1%
1157801.0 1
 
< 0.1%
446209.0 1
 
< 0.1%
450411.33 1
 
< 0.1%
152864.4 1
 
< 0.1%
Other values (90) 90
 
0.9%
(Missing) 9900
99.0%
ValueCountFrequency (%)
29657.4 1
< 0.1%
40023.0 1
< 0.1%
40783.0 1
< 0.1%
40846.0 1
< 0.1%
41890.0 1
< 0.1%
43945.0 1
< 0.1%
44291.0 1
< 0.1%
45018.0 1
< 0.1%
50620.0 1
< 0.1%
53098.13 1
< 0.1%
ValueCountFrequency (%)
8276319.0 1
< 0.1%
5190418.0 1
< 0.1%
4370158.5 1
< 0.1%
2560680.0 1
< 0.1%
2279850.0 1
< 0.1%
1902558.0 1
< 0.1%
1875075.0 1
< 0.1%
1784246.0 1
< 0.1%
1551499.0 1
< 0.1%
1544986.0 1
< 0.1%

비중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)87.8%
Missing9885
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean34654.148
Minimum1.986
Maximum1567010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T06:14:41.104421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.986
5-th percentile2.584
Q13.76
median4.856
Q35.955
95-th percentile48479.02
Maximum1567010
Range1567008
Interquartile range (IQR)2.195

Descriptive statistics

Standard deviation180296.26
Coefficient of variation (CV)5.2027324
Kurtosis49.453021
Mean34654.148
Median Absolute Deviation (MAD)1.136
Skewness6.6125632
Sum3985227
Variance3.250674 × 1010
MonotonicityNot monotonic
2023-12-13T06:14:41.243006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.712 3
 
< 0.1%
4.74 3
 
< 0.1%
3.98 2
 
< 0.1%
2.882 2
 
< 0.1%
5.9 2
 
< 0.1%
3.18 2
 
< 0.1%
2.805 2
 
< 0.1%
9.42 2
 
< 0.1%
5.0 2
 
< 0.1%
4.9 2
 
< 0.1%
Other values (91) 93
 
0.9%
(Missing) 9885
98.9%
ValueCountFrequency (%)
1.986 1
 
< 0.1%
2.013 1
 
< 0.1%
2.03 1
 
< 0.1%
2.22 1
 
< 0.1%
2.27 1
 
< 0.1%
2.5 1
 
< 0.1%
2.62 1
 
< 0.1%
2.63 1
 
< 0.1%
2.649 1
 
< 0.1%
2.712 3
< 0.1%
ValueCountFrequency (%)
1567010.0 1
< 0.1%
807242.0 1
< 0.1%
525591.0 1
< 0.1%
525445.5 1
< 0.1%
397833.0 1
< 0.1%
161564.3 1
< 0.1%
13.9 1
< 0.1%
12.2 1
< 0.1%
9.68 1
< 0.1%
9.42 2
< 0.1%

생성자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9919 
cbmaster@kicet.re.kr
 
76
9.26
 
1
6.2
 
1
7.15
 
1
Other values (2)
 
2

Length

Max length20
Median length4
Mean length4.1215
Min length3

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th rowcbmaster@kicet.re.kr
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9919
99.2%
cbmaster@kicet.re.kr 76
 
0.8%
9.26 1
 
< 0.1%
6.2 1
 
< 0.1%
7.15 1
 
< 0.1%
6.97 1
 
< 0.1%
7.12 1
 
< 0.1%

Length

2023-12-13T06:14:41.416606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:14:41.543366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9919
99.2%
cbmaster@kicet.re.kr 76
 
0.8%
9.26 1
 
< 0.1%
6.2 1
 
< 0.1%
7.15 1
 
< 0.1%
6.97 1
 
< 0.1%
7.12 1
 
< 0.1%

생성일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9924 
2011-08-29
 
75
2011-08-26
 
1

Length

Max length10
Median length4
Mean length4.0456
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row2011-08-29
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9924
99.2%
2011-08-29 75
 
0.8%
2011-08-26 1
 
< 0.1%

Length

2023-12-13T06:14:41.660681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:14:41.752764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9924
99.2%
2011-08-29 75
 
0.8%
2011-08-26 1
 
< 0.1%

수정자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

수정일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2023-12-13T06:14:35.911977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:28.548089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.492278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.367649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.200854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.039191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.971019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.895522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.035290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.026867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:28.653281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.597422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.467088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.300629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.115644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.062270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.982750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.142750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.122702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:28.774151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.716598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.575852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.399524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.199974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.175300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.074433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.249696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.242936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:28.878231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.830371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.693193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.497180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.283978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.294051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.160195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.340690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.352247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.017495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.925613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.792127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.589859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.382870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.390143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.238282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.441720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.469631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.120034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.012931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.877480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.688304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.524384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.505598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.326758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.539694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.579515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.227014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.104198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.958758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.794632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.676268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.603914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.754096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.643125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.681414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.303763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.184960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.022708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.862921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.757153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.710740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.844020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.736921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:36.797878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:29.390526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:30.276534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.100036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:31.937866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:32.852180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:33.800870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:34.936709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:14:35.815908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:14:41.819069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도비열엔탈피기브스 에너지녹는점끓는점용해열기화열비중생성자생성일자
온도1.0000.0960.0760.2120.3600.0000.0000.5790.0000.0000.000
비열0.0961.0000.7790.8450.0000.0000.3050.3790.000NaNNaN
엔탈피0.0760.7791.0000.9540.3460.1840.2240.4530.000NaNNaN
기브스 에너지0.2120.8450.9541.0000.7490.0000.0000.7690.000NaNNaN
녹는점0.3600.0000.3460.7491.0000.7950.3790.785NaN0.000NaN
끓는점0.0000.0000.1840.0000.7951.0000.7320.7250.3000.9420.000
용해열0.0000.3050.2240.0000.3790.7321.0000.322NaN0.000NaN
기화열0.5790.3790.4530.7690.7850.7250.3221.000NaNNaNNaN
비중0.0000.0000.0000.000NaN0.300NaNNaN1.0001.0000.000
생성자0.000NaNNaNNaN0.0000.9420.000NaN1.0001.000NaN
생성일자0.000NaNNaNNaNNaN0.000NaNNaN0.000NaN1.000
2023-12-13T06:14:41.938375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생성자생성일자
생성자1.0001.000
생성일자1.0001.000
2023-12-13T06:14:42.048425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도비열엔탈피기브스 에너지녹는점끓는점용해열기화열비중생성자생성일자
온도1.0000.1480.090-0.2540.079-0.005-0.0650.012-0.0320.0000.000
비열0.1481.000-0.381-0.6080.055-0.0030.2200.257-0.1241.0001.000
엔탈피0.090-0.3811.0000.872-0.145-0.214-0.239-0.3190.3501.0001.000
기브스 에너지-0.254-0.6080.8721.000-0.136-0.199-0.195-0.3300.3361.0001.000
녹는점0.0790.055-0.145-0.1361.0000.9100.7690.7360.2321.000NaN
끓는점-0.005-0.003-0.214-0.1990.9101.0000.6520.8270.2880.2501.000
용해열-0.0650.220-0.239-0.1950.7690.6521.0000.5080.0421.000NaN
기화열0.0120.257-0.319-0.3300.7360.8270.5081.0000.0890.0000.000
비중-0.032-0.1240.3500.3360.2320.2880.0420.0891.0001.0001.000
생성자0.0001.0001.0001.0001.0000.2501.0000.0001.0001.0001.000
생성일자0.0001.0001.0001.000NaN1.000NaN0.0001.0001.0001.000

Missing values

2023-12-13T06:14:36.958095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:14:37.139935image/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.
2023-12-13T06:14:37.302357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

소재시퀀스온도비열엔탈피기브스 에너지녹는점끓는점용해열기화열비중생성자생성일자수정자수정일자
738B10020614041.421023-179751.01-194825.03<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1824B100212160242.89983-129853.66-253666.64<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1155B100219108055.117798-143142.18-288053.46<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1018B10025596059.40839-91222.053-211397.48<NA><NA><NA><NA><NA>cbmaster@kicet.re.kr2011-08-29<NA><NA>
8183B100809186054.560604-508255.3-769281.29<NA><NA><NA><NA><NA><NA><NA><NA><NA>
23604B10066676056.390055-429418.13-567504.84<NA><NA><NA><NA><NA><NA><NA><NA><NA>
14101B1009071940155.64484-1527846.6-2493204.6<NA><NA><NA><NA><NA><NA><NA><NA><NA>
16786B1005141360109.28608-1011318.9-1450947.8<NA><NA><NA><NA><NA><NA><NA><NA><NA>
2942B100229128082.01569-2768.978-328560.69<NA><NA><NA><NA><NA><NA><NA><NA><NA>
23094B10066630052.871075-454639.94-512785.35<NA><NA><NA><NA><NA><NA><NA><NA><NA>
소재시퀀스온도비열엔탈피기브스 에너지녹는점끓는점용해열기화열비중생성자생성일자수정자수정일자
6473B1001111440235.55157-219046.79-893290.44<NA><NA><NA><NA><NA><NA><NA><NA><NA>
3699B100114162060.84350113349.569-213692.46<NA><NA><NA><NA><NA><NA><NA><NA><NA>
5758B100104860180.7395568128.882-197612.9<NA><NA><NA><NA><NA><NA><NA><NA><NA>
16161B100918980133.22068-1778161.1-2194100.4<NA><NA><NA><NA><NA><NA><NA><NA><NA>
5391B100110820311.51429-551279.76-996503.47<NA><NA><NA><NA><NA><NA><NA><NA><NA>
3230B100230114081.621703-12997.403-285323.83<NA><NA><NA><NA><NA><NA><NA><NA><NA>
11892B100868180063.740144-491038.76-824444.14<NA><NA><NA><NA><NA><NA><NA><NA><NA>
12203B100838820140.53978-1116688.5-1401698.2<NA><NA><NA><NA><NA><NA><NA><NA><NA>
21625B100672116081.92766-189892.29-445909.51<NA><NA><NA><NA><NA><NA><NA><NA><NA>
4208B1001301700115.0086915696.992-452897.4<NA><NA><NA><NA><NA><NA><NA><NA><NA>