Overview

Dataset statistics

Number of variables9
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory84.1 B

Variable types

Categorical1
Numeric8

Dataset

DescriptionAPEC 회원국과 구축된 네트워크를 기반으로 우리 기관 및 중소기업의 글로벌화 지원 및 산업협력기회 창출목적으로 구축된 APEC 중소기업혁신센터 홈페이지의 주요 페이지별 방문자수 현황의 정보를 제공합니다.
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15071274/fileData.do

Alerts

메인_Main_ is highly overall correlated with 기관소개_About SMEIC_ and 6 other fieldsHigh correlation
기관소개_About SMEIC_ is highly overall correlated with 메인_Main_ and 2 other fieldsHigh correlation
연구자료_Research_ is highly overall correlated with 메인_Main_ and 2 other fieldsHigh correlation
세미나및포럼_Seminar and Forum_ is highly overall correlated with 메인_Main_ and 2 other fieldsHigh correlation
워크숍_Workshop_ is highly overall correlated with 메인_Main_ and 3 other fieldsHigh correlation
뉴스_News_ is highly overall correlated with 메인_Main_ and 2 other fieldsHigh correlation
아카이브_Archive_ is highly overall correlated with 메인_Main_ and 2 other fieldsHigh correlation
연도 is highly overall correlated with 메인_Main_ and 1 other fieldsHigh correlation
뉴스_News_ has unique valuesUnique
아카이브_Archive_ has unique valuesUnique
연구자료_Research_ has 31 (72.1%) zerosZeros
세미나및포럼_Seminar and Forum_ has 31 (72.1%) zerosZeros
워크숍_Workshop_ has 31 (72.1%) zerosZeros

Reproduction

Analysis started2023-12-12 14:30:50.623785
Analysis finished2023-12-12 14:30:57.642369
Duration7.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2019
12 
2022
12 
2021
11 
2023
8 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 12
27.9%
2022 12
27.9%
2021 11
25.6%
2023 8
18.6%

Length

2023-12-12T23:30:57.708421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:30:57.805718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 12
27.9%
2022 12
27.9%
2021 11
25.6%
2023 8
18.6%

월
Real number (ℝ)

Distinct12
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.255814
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:30:57.902897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q13.5
median6
Q39
95-th percentile11.9
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.3458789
Coefficient of variation (CV)0.5348431
Kurtosis-1.0902835
Mean6.255814
Median Absolute Deviation (MAD)3
Skewness0.1302919
Sum269
Variance11.194906
MonotonicityNot monotonic
2023-12-12T23:30:58.048263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 4
9.3%
3 4
9.3%
4 4
9.3%
5 4
9.3%
6 4
9.3%
7 4
9.3%
8 4
9.3%
1 3
7.0%
9 3
7.0%
10 3
7.0%
Other values (2) 6
14.0%
ValueCountFrequency (%)
1 3
7.0%
2 4
9.3%
3 4
9.3%
4 4
9.3%
5 4
9.3%
6 4
9.3%
7 4
9.3%
8 4
9.3%
9 3
7.0%
10 3
7.0%
ValueCountFrequency (%)
12 3
7.0%
11 3
7.0%
10 3
7.0%
9 3
7.0%
8 4
9.3%
7 4
9.3%
6 4
9.3%
5 4
9.3%
4 4
9.3%
3 4
9.3%

메인_Main_
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9277.0698
Minimum1048
Maximum35436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:30:58.195166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1048
5-th percentile1067
Q11465.5
median9070
Q313755.5
95-th percentile15030.5
Maximum35436
Range34388
Interquartile range (IQR)12290

Descriptive statistics

Standard deviation7099.5827
Coefficient of variation (CV)0.76528288
Kurtosis2.8714737
Mean9277.0698
Median Absolute Deviation (MAD)5114
Skewness1.0362574
Sum398914
Variance50404074
MonotonicityNot monotonic
2023-12-12T23:30:58.379565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1067 2
 
4.7%
1052 1
 
2.3%
13857 1
 
2.3%
13601 1
 
2.3%
12746 1
 
2.3%
12974 1
 
2.3%
12353 1
 
2.3%
13818 1
 
2.3%
15038 1
 
2.3%
35436 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
1048 1
2.3%
1052 1
2.3%
1067 2
4.7%
1069 1
2.3%
1095 1
2.3%
1173 1
2.3%
1177 1
2.3%
1210 1
2.3%
1321 1
2.3%
1395 1
2.3%
ValueCountFrequency (%)
35436 1
2.3%
22611 1
2.3%
15038 1
2.3%
14963 1
2.3%
14458 1
2.3%
14273 1
2.3%
14219 1
2.3%
14184 1
2.3%
13857 1
2.3%
13818 1
2.3%

기관소개_About SMEIC_
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648.34884
Minimum24
Maximum7338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:30:58.536981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile35.1
Q1215
median325
Q3705.5
95-th percentile1024.2
Maximum7338
Range7314
Interquartile range (IQR)490.5

Descriptive statistics

Standard deviation1145.0447
Coefficient of variation (CV)1.7660936
Kurtosis29.13082
Mean648.34884
Median Absolute Deviation (MAD)273
Skewness5.100077
Sum27879
Variance1311127.5
MonotonicityNot monotonic
2023-12-12T23:30:58.687984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
325 2
 
4.7%
249 1
 
2.3%
7338 1
 
2.3%
880 1
 
2.3%
864 1
 
2.3%
625 1
 
2.3%
517 1
 
2.3%
480 1
 
2.3%
598 1
 
2.3%
912 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
24 1
2.3%
34 1
2.3%
35 1
2.3%
36 1
2.3%
45 1
2.3%
55 1
2.3%
64 1
2.3%
65 1
2.3%
115 1
2.3%
137 1
2.3%
ValueCountFrequency (%)
7338 1
2.3%
2786 1
2.3%
1033 1
2.3%
945 1
2.3%
920 1
2.3%
912 1
2.3%
880 1
2.3%
876 1
2.3%
864 1
2.3%
735 1
2.3%

연구자료_Research_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79069767
Minimum0
Maximum5
Zeros31
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:30:58.807483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4566455
Coefficient of variation (CV)1.8422282
Kurtosis1.548942
Mean0.79069767
Median Absolute Deviation (MAD)0
Skewness1.6914093
Sum34
Variance2.1218162
MonotonicityNot monotonic
2023-12-12T23:30:59.270801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 31
72.1%
2 4
 
9.3%
4 4
 
9.3%
1 2
 
4.7%
3 1
 
2.3%
5 1
 
2.3%
ValueCountFrequency (%)
0 31
72.1%
1 2
 
4.7%
2 4
 
9.3%
3 1
 
2.3%
4 4
 
9.3%
5 1
 
2.3%
ValueCountFrequency (%)
5 1
 
2.3%
4 4
 
9.3%
3 1
 
2.3%
2 4
 
9.3%
1 2
 
4.7%
0 31
72.1%

세미나및포럼_Seminar and Forum_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.511628
Minimum0
Maximum127
Zeros31
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:30:59.394454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q376.5
95-th percentile123
Maximum127
Range127
Interquartile range (IQR)76.5

Descriptive statistics

Standard deviation48.769805
Coefficient of variation (CV)1.6525624
Kurtosis-0.65588953
Mean29.511628
Median Absolute Deviation (MAD)0
Skewness1.1199652
Sum1269
Variance2378.4939
MonotonicityNot monotonic
2023-12-12T23:30:59.536366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 31
72.1%
124 2
 
4.7%
107 2
 
4.7%
97 1
 
2.3%
109 1
 
2.3%
127 1
 
2.3%
114 1
 
2.3%
84 1
 
2.3%
99 1
 
2.3%
69 1
 
2.3%
ValueCountFrequency (%)
0 31
72.1%
69 1
 
2.3%
84 1
 
2.3%
97 1
 
2.3%
99 1
 
2.3%
107 2
 
4.7%
108 1
 
2.3%
109 1
 
2.3%
114 1
 
2.3%
124 2
 
4.7%
ValueCountFrequency (%)
127 1
2.3%
124 2
4.7%
114 1
2.3%
109 1
2.3%
108 1
2.3%
107 2
4.7%
99 1
2.3%
97 1
2.3%
84 1
2.3%
69 1
2.3%

워크숍_Workshop_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.627907
Minimum0
Maximum148
Zeros31
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:30:59.663201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3101
95-th percentile137.5
Maximum148
Range148
Interquartile range (IQR)101

Descriptive statistics

Standard deviation55.365032
Coefficient of variation (CV)1.6464014
Kurtosis-0.67607682
Mean33.627907
Median Absolute Deviation (MAD)0
Skewness1.1067588
Sum1446
Variance3065.2868
MonotonicityNot monotonic
2023-12-12T23:30:59.848829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 31
72.1%
109 2
 
4.7%
108 1
 
2.3%
111 1
 
2.3%
138 1
 
2.3%
133 1
 
2.3%
115 1
 
2.3%
130 1
 
2.3%
148 1
 
2.3%
103 1
 
2.3%
Other values (2) 2
 
4.7%
ValueCountFrequency (%)
0 31
72.1%
99 1
 
2.3%
103 1
 
2.3%
108 1
 
2.3%
109 2
 
4.7%
111 1
 
2.3%
115 1
 
2.3%
130 1
 
2.3%
133 1
 
2.3%
138 1
 
2.3%
ValueCountFrequency (%)
148 1
2.3%
143 1
2.3%
138 1
2.3%
133 1
2.3%
130 1
2.3%
115 1
2.3%
111 1
2.3%
109 2
4.7%
108 1
2.3%
103 1
2.3%

뉴스_News_
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4107.3023
Minimum34
Maximum63060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:31:00.030250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile42.6
Q1459
median621
Q33545.5
95-th percentile12788.8
Maximum63060
Range63026
Interquartile range (IQR)3086.5

Descriptive statistics

Standard deviation9949.1413
Coefficient of variation (CV)2.4223056
Kurtosis30.811454
Mean4107.3023
Median Absolute Deviation (MAD)587
Skewness5.2741046
Sum176614
Variance98985413
MonotonicityNot monotonic
2023-12-12T23:31:00.190595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
555 1
 
2.3%
621 1
 
2.3%
3322 1
 
2.3%
8377 1
 
2.3%
4063 1
 
2.3%
2448 1
 
2.3%
2596 1
 
2.3%
2414 1
 
2.3%
4324 1
 
2.3%
3433 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
34 1
2.3%
38 1
2.3%
42 1
2.3%
48 1
2.3%
60 1
2.3%
76 1
2.3%
109 1
2.3%
129 1
2.3%
299 1
2.3%
415 1
2.3%
ValueCountFrequency (%)
63060 1
2.3%
19136 1
2.3%
13279 1
2.3%
8377 1
2.3%
7693 1
2.3%
7289 1
2.3%
5421 1
2.3%
4324 1
2.3%
4063 1
2.3%
3828 1
2.3%

아카이브_Archive_
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3493.4419
Minimum47
Maximum79322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:31:00.339756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile67
Q1339
median487
Q31974
95-th percentile8498.4
Maximum79322
Range79275
Interquartile range (IQR)1635

Descriptive statistics

Standard deviation12113.604
Coefficient of variation (CV)3.4675271
Kurtosis38.966923
Mean3493.4419
Median Absolute Deviation (MAD)440
Skewness6.1309584
Sum150218
Variance1.4673941 × 108
MonotonicityNot monotonic
2023-12-12T23:31:00.536599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
359 1
 
2.3%
350 1
 
2.3%
2264 1
 
2.3%
2999 1
 
2.3%
3508 1
 
2.3%
2137 1
 
2.3%
7296 1
 
2.3%
1578 1
 
2.3%
1812 1
 
2.3%
1363 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
47 1
2.3%
60 1
2.3%
64 1
2.3%
94 1
2.3%
103 1
2.3%
115 1
2.3%
135 1
2.3%
142 1
2.3%
312 1
2.3%
321 1
2.3%
ValueCountFrequency (%)
79322 1
2.3%
13611 1
2.3%
8632 1
2.3%
7296 1
2.3%
3714 1
2.3%
3508 1
2.3%
2999 1
2.3%
2264 1
2.3%
2137 1
2.3%
2099 1
2.3%

Interactions

2023-12-12T23:30:56.647099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:50.902949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.691878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.394839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.598192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.408711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.125989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.855651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.743756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:50.978110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.784304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.492487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.680785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.503039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.201325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.945482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.857094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.072647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.871991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.585737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.801121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.598011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.281969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.067747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.961869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.182342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.954241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.681575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.916410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.701300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.363823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.162518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:57.046472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.298104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.037749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.782125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.016371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.779191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.437866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.255526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:57.132058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.396991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.114374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.233974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.118296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.868208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.524142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.353981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:57.215053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.485994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.213735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.354936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.209711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.961323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.628693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.460218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:57.319052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:51.582709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:52.303056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:53.463593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:54.305995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.043655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:55.727476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:30:56.555212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:31:00.679188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월메인_Main_기관소개_About SMEIC_연구자료_Research_세미나및포럼_Seminar and Forum_워크숍_Workshop_뉴스_News_아카이브_Archive_
연도1.0000.0000.8160.4740.6660.6660.5880.1080.000
월0.0001.0000.0000.0000.0000.0000.0000.0000.269
메인_Main_0.8160.0001.0000.7850.4160.4160.3650.5990.935
기관소개_About SMEIC_0.4740.0000.7851.0000.0000.0000.0000.8490.807
연구자료_Research_0.6660.0000.4160.0001.0000.9470.7770.0000.000
세미나및포럼_Seminar and Forum_0.6660.0000.4160.0000.9471.0000.8540.0000.000
워크숍_Workshop_0.5880.0000.3650.0000.7770.8541.0000.0000.000
뉴스_News_0.1080.0000.5990.8490.0000.0000.0001.0001.000
아카이브_Archive_0.0000.2690.9350.8070.0000.0000.0001.0001.000
2023-12-12T23:31:00.847298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월메인_Main_기관소개_About SMEIC_연구자료_Research_세미나및포럼_Seminar and Forum_워크숍_Workshop_뉴스_News_아카이브_Archive_연도
월1.0000.2040.1910.0360.0080.0470.2700.2510.000
메인_Main_0.2041.0000.659-0.762-0.773-0.7720.6780.6300.656
기관소개_About SMEIC_0.1910.6591.000-0.221-0.217-0.2150.8850.8830.196
연구자료_Research_0.036-0.762-0.2211.0000.9760.969-0.319-0.3170.480
세미나및포럼_Seminar and Forum_0.008-0.773-0.2170.9761.0000.982-0.311-0.3050.480
워크숍_Workshop_0.047-0.772-0.2150.9690.9821.000-0.314-0.3110.506
뉴스_News_0.2700.6780.885-0.319-0.311-0.3141.0000.9240.072
아카이브_Archive_0.2510.6300.883-0.317-0.305-0.3110.9241.0000.000
연도0.0000.6560.1960.4800.4800.5060.0720.0001.000

Missing values

2023-12-12T23:30:57.430367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:30:57.588117image/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

연도월메인_Main_기관소개_About SMEIC_연구자료_Research_세미나및포럼_Seminar and Forum_워크숍_Workshop_뉴스_News_아카이브_Archive_
0201911052249397108555359
12019210482891109111621350
22019311733135124109571395
32019410953252107138509390
42019511773224127133531476
52019610673082114115476396
62019710692764124130415328
72019810672874107148495321
8201991395269284109442353
92019101321305199103490425
연도월메인_Main_기관소개_About SMEIC_연구자료_Research_세미나및포럼_Seminar and Forum_워크숍_Workshop_뉴스_News_아카이브_Archive_
33202211138577338000191368632
342022124232137000602444
35202313907360004264
36202328372115000129312
3720233932155000109103
38202348777340006060
392023590702400048115
40202368629350003847
412023790036500076135
42202388985450003494