Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory88.3 B

Variable types

Categorical3
Numeric7

Alerts

male_rate is highly overall correlated with female_rate and 5 other fieldsHigh correlation
female_rate is highly overall correlated with male_rate and 5 other fieldsHigh correlation
all_n10s_rate is highly overall correlated with male_rate and 3 other fieldsHigh correlation
all_n20s_rate is highly overall correlated with male_rate and 3 other fieldsHigh correlation
all_n30s_rate is highly overall correlated with male_rate and 5 other fieldsHigh correlation
all_n40s_rate is highly overall correlated with male_rate and 2 other fieldsHigh correlation
all_n50s_rate is highly overall correlated with male_rate and 2 other fieldsHigh correlation
reprt_year_cn is highly overall correlated with examin_country_nmHigh correlation
examin_country_nm is highly overall correlated with reprt_year_cnHigh correlation
female_rate has 5 (5.0%) zerosZeros
all_n10s_rate has 12 (12.0%) zerosZeros
all_n20s_rate has 6 (6.0%) zerosZeros
all_n30s_rate has 10 (10.0%) zerosZeros
all_n40s_rate has 20 (20.0%) zerosZeros
all_n50s_rate has 31 (31.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:56:21.275379
Analysis finished2023-12-10 09:56:33.656682
Duration12.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

cause_cn
Categorical

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한국과의 정치?외교적 갈등 때문
12 
지나치게 상업적임
11 
지나치게 자극적?선정적
11 
한국의 국민성이 좋지 않음
11 
자국 콘텐츠 산업보호 필요
11 
Other values (5)
44 

Length

Max length25
Median length17
Mean length14.77
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지나치게 상업적임
2nd row지나치게 자극적?선정적
3rd row한국과의 정치?외교적 갈등 때문
4th row한국의 국민성이 좋지 않음
5th row자국 콘텐츠 산업보호 필요

Common Values

ValueCountFrequency (%)
한국과의 정치?외교적 갈등 때문 12
12.0%
지나치게 상업적임 11
11.0%
지나치게 자극적?선정적 11
11.0%
한국의 국민성이 좋지 않음 11
11.0%
자국 콘텐츠 산업보호 필요 11
11.0%
남북 북단?북한의 국제적인 위협관련 보도 때문 11
11.0%
한국과의 역사적인 관계 때문 11
11.0%
획일적이고 식상함 10
10.0%
한류스타?유명인의 부적절한 언행 때문 10
10.0%
기타 2
 
2.0%

Length

2023-12-10T18:56:33.780766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:56:34.122245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
때문 44
 
12.5%
한국과의 23
 
6.5%
지나치게 22
 
6.2%
갈등 12
 
3.4%
정치?외교적 12
 
3.4%
산업보호 11
 
3.1%
관계 11
 
3.1%
역사적인 11
 
3.1%
보도 11
 
3.1%
위협관련 11
 
3.1%
Other values (18) 184
52.3%

male_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.866
Minimum0
Maximum38
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:34.691044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.19
Q14.775
median9.1
Q314
95-th percentile26.715
Maximum38
Range38
Interquartile range (IQR)9.225

Descriptive statistics

Standard deviation8.0675709
Coefficient of variation (CV)0.74246005
Kurtosis1.5485305
Mean10.866
Median Absolute Deviation (MAD)4.7
Skewness1.3042041
Sum1086.6
Variance65.085701
MonotonicityNot monotonic
2023-12-10T18:56:35.161282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.5 4
 
4.0%
3.2 4
 
4.0%
4.3 4
 
4.0%
7.0 3
 
3.0%
14.0 3
 
3.0%
6.3 3
 
3.0%
2.0 3
 
3.0%
11.6 3
 
3.0%
17.4 3
 
3.0%
4.4 3
 
3.0%
Other values (51) 67
67.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
1.6 1
 
1.0%
2.0 3
3.0%
2.2 2
2.0%
2.3 2
2.0%
2.9 2
2.0%
3.0 1
 
1.0%
3.2 4
4.0%
3.3 1
 
1.0%
4.3 4
4.0%
ValueCountFrequency (%)
38.0 1
1.0%
36.7 1
1.0%
33.3 1
1.0%
29.4 1
1.0%
27.0 1
1.0%
26.7 1
1.0%
26.1 1
1.0%
25.4 1
1.0%
25.0 1
1.0%
23.9 1
1.0%

female_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.703
Minimum0
Maximum39.1
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:35.598786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.235
Q14.8
median8.65
Q314.65
95-th percentile28.6
Maximum39.1
Range39.1
Interquartile range (IQR)9.85

Descriptive statistics

Standard deviation8.359673
Coefficient of variation (CV)0.78105886
Kurtosis1.4547437
Mean10.703
Median Absolute Deviation (MAD)4.65
Skewness1.246152
Sum1070.3
Variance69.884132
MonotonicityNot monotonic
2023-12-10T18:56:36.416578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
10.0 4
 
4.0%
2.9 3
 
3.0%
6.8 3
 
3.0%
10.9 3
 
3.0%
3.6 2
 
2.0%
28.6 2
 
2.0%
4.8 2
 
2.0%
5.2 2
 
2.0%
13.0 2
 
2.0%
Other values (57) 72
72.0%
ValueCountFrequency (%)
0.0 5
5.0%
1.3 1
 
1.0%
1.4 2
 
2.0%
1.6 1
 
1.0%
1.7 2
 
2.0%
2.7 1
 
1.0%
2.9 3
3.0%
3.1 2
 
2.0%
3.2 1
 
1.0%
3.6 2
 
2.0%
ValueCountFrequency (%)
39.1 1
1.0%
35.0 1
1.0%
34.3 1
1.0%
30.8 1
1.0%
28.6 2
2.0%
27.3 1
1.0%
25.4 1
1.0%
23.5 1
1.0%
22.9 1
1.0%
22.1 1
1.0%

all_n10s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.898
Minimum0
Maximum35.3
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:36.745362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4
median10.05
Q314.8
95-th percentile25.3
Maximum35.3
Range35.3
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation8.3874483
Coefficient of variation (CV)0.76963189
Kurtosis0.80421375
Mean10.898
Median Absolute Deviation (MAD)4.7
Skewness0.95867942
Sum1089.8
Variance70.349289
MonotonicityNot monotonic
2023-12-10T18:56:37.035791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
12.0%
5.7 6
 
6.0%
5.9 4
 
4.0%
5.4 4
 
4.0%
14.8 4
 
4.0%
10.9 3
 
3.0%
3.3 3
 
3.0%
2.9 3
 
3.0%
13.0 3
 
3.0%
11.8 3
 
3.0%
Other values (43) 55
55.0%
ValueCountFrequency (%)
0.0 12
12.0%
2.2 1
 
1.0%
2.9 3
 
3.0%
3.1 1
 
1.0%
3.3 3
 
3.0%
3.7 1
 
1.0%
3.9 1
 
1.0%
4.9 1
 
1.0%
5.4 4
 
4.0%
5.7 6
6.0%
ValueCountFrequency (%)
35.3 1
1.0%
34.3 2
2.0%
33.3 1
1.0%
31.0 1
1.0%
25.0 1
1.0%
24.3 1
1.0%
23.5 1
1.0%
22.9 1
1.0%
22.0 2
2.0%
21.9 1
1.0%

all_n20s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.807
Minimum0
Maximum32.4
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:37.341872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.7
median8.3
Q316.225
95-th percentile24.27
Maximum32.4
Range32.4
Interquartile range (IQR)11.525

Descriptive statistics

Standard deviation7.8753965
Coefficient of variation (CV)0.72873105
Kurtosis-0.29685413
Mean10.807
Median Absolute Deviation (MAD)5.05
Skewness0.7593206
Sum1080.7
Variance62.02187
MonotonicityNot monotonic
2023-12-10T18:56:37.709867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
2.8 4
 
4.0%
9.1 4
 
4.0%
4.0 4
 
4.0%
4.5 4
 
4.0%
5.4 3
 
3.0%
10.0 3
 
3.0%
8.3 3
 
3.0%
13.9 3
 
3.0%
18.9 3
 
3.0%
Other values (46) 63
63.0%
ValueCountFrequency (%)
0.0 6
6.0%
2.3 2
 
2.0%
2.6 3
3.0%
2.7 1
 
1.0%
2.8 4
4.0%
4.0 4
4.0%
4.5 4
4.0%
4.7 2
 
2.0%
5.1 2
 
2.0%
5.3 2
 
2.0%
ValueCountFrequency (%)
32.4 1
1.0%
30.6 1
1.0%
28.0 1
1.0%
27.8 1
1.0%
25.6 1
1.0%
24.2 1
1.0%
24.0 2
2.0%
23.7 1
1.0%
22.7 2
2.0%
22.2 1
1.0%

all_n30s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.759
Minimum0
Maximum48.1
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:38.180019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7.95
Q316.25
95-th percentile26.43
Maximum48.1
Range48.1
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation9.0679934
Coefficient of variation (CV)0.84282864
Kurtosis2.0273762
Mean10.759
Median Absolute Deviation (MAD)5.35
Skewness1.2533908
Sum1075.9
Variance82.228504
MonotonicityNot monotonic
2023-12-10T18:56:38.510767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
6.7 5
 
5.0%
4.0 4
 
4.0%
2.2 4
 
4.0%
10.5 4
 
4.0%
1.9 3
 
3.0%
5.3 3
 
3.0%
8.0 3
 
3.0%
5.9 3
 
3.0%
7.1 3
 
3.0%
Other values (46) 58
58.0%
ValueCountFrequency (%)
0.0 10
10.0%
1.9 3
 
3.0%
2.2 4
 
4.0%
2.4 1
 
1.0%
2.6 2
 
2.0%
3.7 2
 
2.0%
4.0 4
 
4.0%
4.3 3
 
3.0%
5.3 3
 
3.0%
5.9 3
 
3.0%
ValueCountFrequency (%)
48.1 1
1.0%
32.4 1
1.0%
32.1 1
1.0%
32.0 1
1.0%
28.9 1
1.0%
26.3 2
2.0%
26.1 1
1.0%
23.5 1
1.0%
23.4 1
1.0%
22.6 1
1.0%

all_n40s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.964
Minimum0
Maximum52.9
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:38.827781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.9
median7.1
Q315.8
95-th percentile41
Maximum52.9
Range52.9
Interquartile range (IQR)12.9

Descriptive statistics

Standard deviation11.83691
Coefficient of variation (CV)1.079616
Kurtosis3.1476718
Mean10.964
Median Absolute Deviation (MAD)7.1
Skewness1.7542129
Sum1096.4
Variance140.11243
MonotonicityNot monotonic
2023-12-10T18:56:39.095563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 20
20.0%
15.8 7
 
7.0%
6.7 5
 
5.0%
5.3 4
 
4.0%
2.5 4
 
4.0%
7.1 4
 
4.0%
5.9 4
 
4.0%
8.0 3
 
3.0%
20.0 3
 
3.0%
13.3 3
 
3.0%
Other values (29) 43
43.0%
ValueCountFrequency (%)
0.0 20
20.0%
2.5 4
 
4.0%
2.6 1
 
1.0%
3.0 2
 
2.0%
3.3 3
 
3.0%
3.7 2
 
2.0%
4.0 2
 
2.0%
5.3 4
 
4.0%
5.9 4
 
4.0%
6.1 1
 
1.0%
ValueCountFrequency (%)
52.9 1
 
1.0%
47.5 1
 
1.0%
46.7 3
3.0%
40.7 1
 
1.0%
33.3 1
 
1.0%
27.3 1
 
1.0%
26.3 1
 
1.0%
24.0 2
2.0%
23.7 1
 
1.0%
21.4 2
2.0%

all_n50s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.489
Minimum0
Maximum53.8
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:39.385929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.8
Q313.475
95-th percentile37.5
Maximum53.8
Range53.8
Interquartile range (IQR)13.475

Descriptive statistics

Standard deviation11.873048
Coefficient of variation (CV)1.1319523
Kurtosis2.4841702
Mean10.489
Median Absolute Deviation (MAD)7.8
Skewness1.6050905
Sum1048.9
Variance140.96927
MonotonicityNot monotonic
2023-12-10T18:56:39.783252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 31
31.0%
11.1 6
 
6.0%
5.6 4
 
4.0%
16.7 4
 
4.0%
12.5 4
 
4.0%
7.7 4
 
4.0%
11.8 3
 
3.0%
10.0 3
 
3.0%
5.9 3
 
3.0%
4.2 3
 
3.0%
Other values (26) 35
35.0%
ValueCountFrequency (%)
0.0 31
31.0%
4.0 2
 
2.0%
4.2 3
 
3.0%
5.0 1
 
1.0%
5.3 2
 
2.0%
5.6 4
 
4.0%
5.9 3
 
3.0%
7.7 4
 
4.0%
7.9 2
 
2.0%
8.0 2
 
2.0%
ValueCountFrequency (%)
53.8 1
1.0%
47.1 1
1.0%
45.0 1
1.0%
42.9 1
1.0%
37.5 2
2.0%
33.3 2
2.0%
29.2 1
1.0%
28.6 1
1.0%
27.8 2
2.0%
25.0 1
1.0%

reprt_year_cn
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021
26 
2020
18 
2019
18 
2016-2017
18 
2018
17 
Other values (3)
3 

Length

Max length9
Median length4
Mean length4.9
Min length4

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row2021
2nd row2028
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 26
26.0%
2020 18
18.0%
2019 18
18.0%
2016-2017 18
18.0%
2018 17
17.0%
2028 1
 
1.0%
2029 1
 
1.0%
2030 1
 
1.0%

Length

2023-12-10T18:56:40.271438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:56:40.572871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 26
26.0%
2020 18
18.0%
2019 18
18.0%
2016-2017 18
18.0%
2018 17
17.0%
2028 1
 
1.0%
2029 1
 
1.0%
2030 1
 
1.0%

examin_country_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일본
46 
중국
42 
대만
9 
아르헨티나
 
3

Length

Max length5
Median length2
Mean length2.09
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중국
2nd row아르헨티나
3rd row중국
4th row중국
5th row중국

Common Values

ValueCountFrequency (%)
일본 46
46.0%
중국 42
42.0%
대만 9
 
9.0%
아르헨티나 3
 
3.0%

Length

2023-12-10T18:56:40.917329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:56:41.131515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일본 46
46.0%
중국 42
42.0%
대만 9
 
9.0%
아르헨티나 3
 
3.0%

Interactions

2023-12-10T18:56:31.677334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:22.029155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:23.791796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:25.425208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:27.050607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:28.581077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:30.173652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:31.894773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:22.201207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:24.058064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:25.650348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:27.247662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:28.843399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:30.378919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:32.108562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:22.370848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:24.264849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:25.899846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:27.433001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:29.078380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:30.636678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:32.279410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:22.545668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:24.558412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:26.154773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:27.648691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:29.356269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:30.856322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:32.476532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:22.712040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:24.741644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:26.375493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:27.937271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:29.591741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:31.090713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:32.655952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:22.866373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:25.000853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:26.591741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:28.146258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:29.776212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:31.303947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:32.854398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:23.521626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:25.203747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:26.831320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:28.321647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:29.969039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:31.495011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:56:41.296135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
cause_cnmale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratereprt_year_cnexamin_country_nm
cause_cn1.0000.4010.0000.2360.5000.3520.1460.3250.1720.000
male_rate0.4011.0000.9120.8420.8190.7520.8810.9120.0000.000
female_rate0.0000.9121.0000.7920.8470.7360.8390.8910.0000.207
all_n10s_rate0.2360.8420.7921.0000.7840.5430.8280.6750.0000.000
all_n20s_rate0.5000.8190.8470.7841.0000.7040.7350.8060.0000.000
all_n30s_rate0.3520.7520.7360.5430.7041.0000.6720.6970.0000.000
all_n40s_rate0.1460.8810.8390.8280.7350.6721.0000.8470.0000.000
all_n50s_rate0.3250.9120.8910.6750.8060.6970.8471.0000.0000.000
reprt_year_cn0.1720.0000.0000.0000.0000.0000.0000.0001.0000.911
examin_country_nm0.0000.0000.2070.0000.0000.0000.0000.0000.9111.000
2023-12-10T18:56:41.570150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
examin_country_nmcause_cnreprt_year_cn
examin_country_nm1.0000.0000.607
cause_cn0.0001.0000.076
reprt_year_cn0.6070.0761.000
2023-12-10T18:56:41.754895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
male_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratecause_cnreprt_year_cnexamin_country_nm
male_rate1.0000.7220.7450.7480.8340.7080.5760.1470.0000.000
female_rate0.7221.0000.7190.7510.7140.6860.6230.0000.0000.117
all_n10s_rate0.7450.7191.0000.5510.5370.4870.4580.0670.0000.000
all_n20s_rate0.7480.7510.5511.0000.5780.4700.4240.1610.0000.000
all_n30s_rate0.8340.7140.5370.5781.0000.6340.5500.1720.0000.000
all_n40s_rate0.7080.6860.4870.4700.6341.0000.4990.0330.0000.000
all_n50s_rate0.5760.6230.4580.4240.5500.4991.0000.1000.0000.000
cause_cn0.1470.0000.0670.1610.1720.0330.1001.0000.0760.000
reprt_year_cn0.0000.0000.0000.0000.0000.0000.0000.0761.0000.607
examin_country_nm0.0000.1170.0000.0000.0000.0000.0000.0000.6071.000

Missing values

2023-12-10T18:56:33.230989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:56:33.549535image/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

cause_cnmale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratereprt_year_cnexamin_country_nm
0지나치게 상업적임15.923.521.616.321.418.216.02021중국
1지나치게 자극적?선정적9.10.05.95.97.10.00.02028아르헨티나
2한국과의 정치?외교적 갈등 때문12.49.93.918.611.915.28.02021중국
3한국의 국민성이 좋지 않음11.511.19.89.321.43.012.02021중국
4자국 콘텐츠 산업보호 필요11.59.915.74.72.427.34.02021중국
5남북 북단?북한의 국제적인 위협관련 보도 때문11.57.47.816.37.16.112.02021중국
6한국과의 역사적인 관계 때문11.56.211.87.09.512.14.02021중국
7한국과의 정치?외교적 갈등 때문6.10.00.00.07.116.70.02029아르헨티나
8획일적이고 식상함4.47.411.84.70.03.08.02021중국
9지나치게 상업적임33.339.134.332.426.347.542.92020중국
cause_cnmale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratereprt_year_cnexamin_country_nm
90자국 콘텐츠 산업보호 필요2.01.40.05.60.00.04.22016-2017일본
91지나치게 상업적임22.230.835.328.016.021.425.02021대만
92한국의 국민성이 좋지 않음25.415.423.520.032.014.312.52021대만
93한류스타?유명인의 부적절한 언행 때문19.011.517.624.020.00.012.52021대만
94남북 북단?북한의 국제적인 위협관련 보도 때문11.111.511.88.04.014.337.52021대만
95지나치게 자극적?선정적3.215.45.94.04.021.40.02021대만
96한국과의 정치?외교적 갈등 때문6.33.80.08.08.07.10.02021대만
97자국 콘텐츠 산업보호 필요6.30.05.90.04.07.112.52021대만
98획일적이고 식상함4.83.80.04.08.07.10.02021대만
99한국과의 역사적인 관계 때문1.67.70.04.04.07.10.02021대만