Overview

Dataset statistics

Number of variables10
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory90.9 B

Variable types

Text1
Categorical1
Numeric8

Dataset

Description대구광역시 수출지원시스템에 등록된 기업의 해외규격인증 신청현황 데이터입니다.규격별 연간 신청현황 통계데이터 입니다.
Author대구광역시
URLhttps://www.data.go.kr/data/15087836/fileData.do

Alerts

2015 is highly overall correlated with 2016 and 2 other fieldsHigh correlation
2016 is highly overall correlated with 2015 and 3 other fieldsHigh correlation
2017 is highly overall correlated with 2015 and 2 other fieldsHigh correlation
2018 is highly overall correlated with 2015 and 2 other fieldsHigh correlation
2019 is highly overall correlated with 2016High correlation
2014 is highly imbalanced (81.2%)Imbalance
해외규격인증 has unique valuesUnique
2015 has 54 (78.3%) zerosZeros
2016 has 55 (79.7%) zerosZeros
2017 has 51 (73.9%) zerosZeros
2018 has 52 (75.4%) zerosZeros
2019 has 46 (66.7%) zerosZeros
2020 has 32 (46.4%) zerosZeros
2021 has 48 (69.6%) zerosZeros
2022 has 57 (82.6%) zerosZeros

Reproduction

Analysis started2023-12-12 20:38:41.109025
Analysis finished2023-12-12 20:38:48.842532
Duration7.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

해외규격인증
Text

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-13T05:38:49.026139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.2173913
Min length2

Characters and Unicode

Total characters291
Distinct characters40
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)100.0%

Sample

1st rowJAS
2nd rowABS
3rd rowANSI
4th rowAPI
5th rowARAI
ValueCountFrequency (%)
jas 1
 
1.4%
gost 1
 
1.4%
okcpst 1
 
1.4%
oekotx 1
 
1.4%
nrtl 1
 
1.4%
nongmo 1
 
1.4%
mpa 1
 
1.4%
lr 1
 
1.4%
iso270 1
 
1.4%
lfgb 1
 
1.4%
Other values (59) 59
85.5%
2023-12-13T05:38:49.453895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 30
 
10.3%
A 30
 
10.3%
C 29
 
10.0%
E 19
 
6.5%
N 16
 
5.5%
R 16
 
5.5%
O 14
 
4.8%
G 13
 
4.5%
P 12
 
4.1%
M 12
 
4.1%
Other values (30) 100
34.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 272
93.5%
Decimal Number 11
 
3.8%
Other Letter 8
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 30
 
11.0%
A 30
 
11.0%
C 29
 
10.7%
E 19
 
7.0%
N 16
 
5.9%
R 16
 
5.9%
O 14
 
5.1%
G 13
 
4.8%
P 12
 
4.4%
M 12
 
4.4%
Other values (15) 81
29.8%
Other Letter
ValueCountFrequency (%)
트 1
12.5%
허 1
12.5%
인 1
12.5%
품 1
12.5%
식 1
12.5%
남 1
12.5%
베 1
12.5%
가 1
12.5%
Decimal Number
ValueCountFrequency (%)
2 3
27.3%
0 2
18.2%
9 2
18.2%
1 1
 
9.1%
7 1
 
9.1%
5 1
 
9.1%
3 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 272
93.5%
Common 11
 
3.8%
Hangul 8
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 30
 
11.0%
A 30
 
11.0%
C 29
 
10.7%
E 19
 
7.0%
N 16
 
5.9%
R 16
 
5.9%
O 14
 
5.1%
G 13
 
4.8%
P 12
 
4.4%
M 12
 
4.4%
Other values (15) 81
29.8%
Hangul
ValueCountFrequency (%)
트 1
12.5%
허 1
12.5%
인 1
12.5%
품 1
12.5%
식 1
12.5%
남 1
12.5%
베 1
12.5%
가 1
12.5%
Common
ValueCountFrequency (%)
2 3
27.3%
0 2
18.2%
9 2
18.2%
1 1
 
9.1%
7 1
 
9.1%
5 1
 
9.1%
3 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
97.3%
Hangul 8
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 30
 
10.6%
A 30
 
10.6%
C 29
 
10.2%
E 19
 
6.7%
N 16
 
5.7%
R 16
 
5.7%
O 14
 
4.9%
G 13
 
4.6%
P 12
 
4.2%
M 12
 
4.2%
Other values (22) 92
32.5%
Hangul
ValueCountFrequency (%)
트 1
12.5%
허 1
12.5%
인 1
12.5%
품 1
12.5%
식 1
12.5%
남 1
12.5%
베 1
12.5%
가 1
12.5%

2014
Categorical

IMBALANCE 

Distinct3
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size684.0 B
0
66 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 66
95.7%
1 2
 
2.9%
2 1
 
1.4%

Length

2023-12-13T05:38:49.606528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:38:49.734299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66
95.7%
1 2
 
2.9%
2 1
 
1.4%

2015
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057971
Minimum0
Maximum36
Zeros54
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:49.876974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.5077843
Coefficient of variation (CV)4.2607824
Kurtosis55.026585
Mean1.057971
Median Absolute Deviation (MAD)0
Skewness7.1452485
Sum73
Variance20.320119
MonotonicityNot monotonic
2023-12-13T05:38:50.025944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 54
78.3%
1 5
 
7.2%
2 5
 
7.2%
4 2
 
2.9%
36 1
 
1.4%
9 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 54
78.3%
1 5
 
7.2%
2 5
 
7.2%
4 2
 
2.9%
5 1
 
1.4%
9 1
 
1.4%
36 1
 
1.4%
ValueCountFrequency (%)
36 1
 
1.4%
9 1
 
1.4%
5 1
 
1.4%
4 2
 
2.9%
2 5
 
7.2%
1 5
 
7.2%
0 54
78.3%

2016
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95652174
Minimum0
Maximum26
Zeros55
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:50.152799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.6
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.549792
Coefficient of variation (CV)3.7111461
Kurtosis38.266576
Mean0.95652174
Median Absolute Deviation (MAD)0
Skewness5.8366409
Sum66
Variance12.601023
MonotonicityNot monotonic
2023-12-13T05:38:50.286831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 55
79.7%
1 6
 
8.7%
2 2
 
2.9%
26 1
 
1.4%
3 1
 
1.4%
6 1
 
1.4%
4 1
 
1.4%
12 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 55
79.7%
1 6
 
8.7%
2 2
 
2.9%
3 1
 
1.4%
4 1
 
1.4%
5 1
 
1.4%
6 1
 
1.4%
12 1
 
1.4%
26 1
 
1.4%
ValueCountFrequency (%)
26 1
 
1.4%
12 1
 
1.4%
6 1
 
1.4%
5 1
 
1.4%
4 1
 
1.4%
3 1
 
1.4%
2 2
 
2.9%
1 6
 
8.7%
0 55
79.7%

2017
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89855072
Minimum0
Maximum27
Zeros51
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:50.422548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.2
Maximum27
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.3917934
Coefficient of variation (CV)3.7747378
Kurtosis53.328517
Mean0.89855072
Median Absolute Deviation (MAD)0
Skewness6.98058
Sum62
Variance11.504263
MonotonicityNot monotonic
2023-12-13T05:38:50.544075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 51
73.9%
1 8
 
11.6%
2 6
 
8.7%
27 1
 
1.4%
4 1
 
1.4%
6 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 51
73.9%
1 8
 
11.6%
2 6
 
8.7%
4 1
 
1.4%
5 1
 
1.4%
6 1
 
1.4%
27 1
 
1.4%
ValueCountFrequency (%)
27 1
 
1.4%
6 1
 
1.4%
5 1
 
1.4%
4 1
 
1.4%
2 6
 
8.7%
1 8
 
11.6%
0 51
73.9%

2018
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76811594
Minimum0
Maximum22
Zeros52
Zeros (%)75.4%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:50.690749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.901039
Coefficient of variation (CV)3.7768244
Kurtosis44.067708
Mean0.76811594
Median Absolute Deviation (MAD)0
Skewness6.3190227
Sum53
Variance8.4160273
MonotonicityNot monotonic
2023-12-13T05:38:50.817595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 52
75.4%
1 11
 
15.9%
2 3
 
4.3%
22 1
 
1.4%
9 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 52
75.4%
1 11
 
15.9%
2 3
 
4.3%
5 1
 
1.4%
9 1
 
1.4%
22 1
 
1.4%
ValueCountFrequency (%)
22 1
 
1.4%
9 1
 
1.4%
5 1
 
1.4%
2 3
 
4.3%
1 11
 
15.9%
0 52
75.4%

2019
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3333333
Minimum0
Maximum29
Zeros46
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:50.954781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4.6
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.1326059
Coefficient of variation (CV)3.0994544
Kurtosis31.555481
Mean1.3333333
Median Absolute Deviation (MAD)0
Skewness5.2823904
Sum92
Variance17.078431
MonotonicityNot monotonic
2023-12-13T05:38:51.103039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 46
66.7%
1 11
 
15.9%
2 5
 
7.2%
4 2
 
2.9%
29 1
 
1.4%
3 1
 
1.4%
15 1
 
1.4%
11 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 46
66.7%
1 11
 
15.9%
2 5
 
7.2%
3 1
 
1.4%
4 2
 
2.9%
5 1
 
1.4%
11 1
 
1.4%
15 1
 
1.4%
29 1
 
1.4%
ValueCountFrequency (%)
29 1
 
1.4%
15 1
 
1.4%
11 1
 
1.4%
5 1
 
1.4%
4 2
 
2.9%
3 1
 
1.4%
2 5
 
7.2%
1 11
 
15.9%
0 46
66.7%

2020
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5652174
Minimum0
Maximum18
Zeros32
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:51.330148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.3143106
Coefficient of variation (CV)2.1174762
Kurtosis14.104307
Mean1.5652174
Median Absolute Deviation (MAD)1
Skewness3.6324329
Sum108
Variance10.984655
MonotonicityNot monotonic
2023-12-13T05:38:51.458241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 32
46.4%
1 24
34.8%
2 3
 
4.3%
6 2
 
2.9%
3 2
 
2.9%
5 2
 
2.9%
16 1
 
1.4%
12 1
 
1.4%
4 1
 
1.4%
18 1
 
1.4%
ValueCountFrequency (%)
0 32
46.4%
1 24
34.8%
2 3
 
4.3%
3 2
 
2.9%
4 1
 
1.4%
5 2
 
2.9%
6 2
 
2.9%
12 1
 
1.4%
16 1
 
1.4%
18 1
 
1.4%
ValueCountFrequency (%)
18 1
 
1.4%
16 1
 
1.4%
12 1
 
1.4%
6 2
 
2.9%
5 2
 
2.9%
4 1
 
1.4%
3 2
 
2.9%
2 3
 
4.3%
1 24
34.8%
0 32
46.4%

2021
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1884058
Minimum0
Maximum17
Zeros48
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:51.607670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7.4
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.1682031
Coefficient of variation (CV)2.665927
Kurtosis14.254803
Mean1.1884058
Median Absolute Deviation (MAD)0
Skewness3.7026505
Sum82
Variance10.037511
MonotonicityNot monotonic
2023-12-13T05:38:51.756437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 48
69.6%
1 11
 
15.9%
4 2
 
2.9%
2 2
 
2.9%
15 1
 
1.4%
5 1
 
1.4%
10 1
 
1.4%
17 1
 
1.4%
3 1
 
1.4%
9 1
 
1.4%
ValueCountFrequency (%)
0 48
69.6%
1 11
 
15.9%
2 2
 
2.9%
3 1
 
1.4%
4 2
 
2.9%
5 1
 
1.4%
9 1
 
1.4%
10 1
 
1.4%
15 1
 
1.4%
17 1
 
1.4%
ValueCountFrequency (%)
17 1
 
1.4%
15 1
 
1.4%
10 1
 
1.4%
9 1
 
1.4%
5 1
 
1.4%
4 2
 
2.9%
3 1
 
1.4%
2 2
 
2.9%
1 11
 
15.9%
0 48
69.6%

2022
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50724638
Minimum0
Maximum13
Zeros57
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T05:38:51.902573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.6
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7625449
Coefficient of variation (CV)3.4747313
Kurtosis38.241898
Mean0.50724638
Median Absolute Deviation (MAD)0
Skewness5.7380604
Sum35
Variance3.1065644
MonotonicityNot monotonic
2023-12-13T05:38:52.057073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 57
82.6%
1 5
 
7.2%
2 3
 
4.3%
3 2
 
2.9%
13 1
 
1.4%
5 1
 
1.4%
ValueCountFrequency (%)
0 57
82.6%
1 5
 
7.2%
2 3
 
4.3%
3 2
 
2.9%
5 1
 
1.4%
13 1
 
1.4%
ValueCountFrequency (%)
13 1
 
1.4%
5 1
 
1.4%
3 2
 
2.9%
2 3
 
4.3%
1 5
 
7.2%
0 57
82.6%

Interactions

2023-12-13T05:38:47.440833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:41.464238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.592656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.412358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.175134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.965945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.756830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.539948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.557980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:41.587944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.712976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.509661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.293730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.065875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.850479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.650323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.655150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:41.670151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.791984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.607774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.418302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.170011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.943279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.779492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.762728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.089832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.884820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.692378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.493665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.258172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.031897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.898279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.851322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.195755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.962549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.765712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.570272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.369351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.119903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.033383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.955424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.293496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.060277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.872702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.663608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.463500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.215097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.147566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:48.053774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.383212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.174110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.993120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.759362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.561055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.327845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.238047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:48.171757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:42.477123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:43.286170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.083852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:44.868122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:45.665546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:46.449677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:38:47.353194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:38:52.153677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
해외규격인증201420152016201720182019202020212022
해외규격인증1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20141.0001.0000.3630.0000.0000.0000.0000.0000.0000.000
20151.0000.3631.0000.8470.9460.9280.8780.7980.8540.833
20161.0000.0000.8471.0000.9020.8800.9920.8640.8740.975
20171.0000.0000.9460.9021.0000.9800.9021.0000.9700.830
20181.0000.0000.9280.8800.9801.0000.8680.9060.9790.740
20191.0000.0000.8780.9920.9020.8681.0000.8850.8700.975
20201.0000.0000.7980.8641.0000.9060.8851.0000.8790.840
20211.0000.0000.8540.8740.9700.9790.8700.8791.0000.818
20221.0000.0000.8330.9750.8300.7400.9750.8400.8181.000
2023-12-13T05:38:52.311430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201520162017201820192020202120222014
20151.0000.5820.5700.5540.4520.0880.3280.2740.349
20160.5821.0000.5160.5410.5310.2170.3620.2230.000
20170.5700.5161.0000.6460.4120.1540.3260.2570.000
20180.5540.5410.6461.0000.4660.0990.3050.1400.000
20190.4520.5310.4120.4661.0000.2990.3570.1940.000
20200.0880.2170.1540.0990.2991.0000.4380.2610.000
20210.3280.3620.3260.3050.3570.4381.0000.4530.000
20220.2740.2230.2570.1400.1940.2610.4531.0000.000
20140.3490.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T05:38:48.645631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:38:48.796563image/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

해외규격인증201420152016201720182019202020212022
0JAS000100000
1ABS000000010
2ANSI000000100
3API000011100
4ARAI100000000
5ASME100000100
6BHMA000100000
7BLSIGN000000210
8BPOM000002100
9BSI010000000
해외규격인증201420152016201720182019202020212022
59SMARK000000010
60SNI000010000
61TELEC000001000
62TFDA000000100
63WPS000000100
64WRAS000000100
65GREENGUARD000000001
66JAPANMIC000000001
67NMPA000000003
68베트남식품인허가000000002