Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.6 KiB
Average record size in memory98.3 B

Variable types

Categorical3
Numeric8

Alerts

issue_cn is highly overall correlated with reprt_year_cnHigh correlation
reprt_year_cn is highly overall correlated with issue_cnHigh correlation
all_total_co is highly overall correlated with male_rate and 6 other fieldsHigh correlation
male_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
female_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
all_n10s_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
all_n20s_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
all_n30s_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
all_n40s_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
all_n50s_rate is highly overall correlated with all_total_co and 6 other fieldsHigh correlation
reprt_year_cn is highly imbalanced (53.0%)Imbalance

Reproduction

Analysis started2023-12-10 10:15:17.831551
Analysis finished2023-12-10 10:15:29.479623
Duration11.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

issue_cn
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
북미 한반도 비핵화 협상
17 
트럼프 대통령 방한 + 정상회담
17 
버닝썬 사건
17 
일본-한국 무역갈등
16 
지소미아(GSOMIA) 파기 결정
15 
Other values (9)
18 

Length

Max length23
Median length17
Mean length12.98
Min length4

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row북미 한반도 비핵화 협상
2nd row촛불집회
3rd row트럼프 대통령 방한 + 정상회담
4th row버닝썬 사건
5th row지소미아(GSOMIA) 파기 결정

Common Values

ValueCountFrequency (%)
북미 한반도 비핵화 협상 17
17.0%
트럼프 대통령 방한 + 정상회담 17
17.0%
버닝썬 사건 17
17.0%
일본-한국 무역갈등 16
16.0%
지소미아(GSOMIA) 파기 결정 15
15.0%
북한의 핵∙미사일 위협 3
 
3.0%
위안부 문제로 인한 한일갈등 3
 
3.0%
남북정상회담에 따른 남북 화해무드 3
 
3.0%
위의 보기 중 전혀 없음 2
 
2.0%
2018 평창 동계올림픽 2
 
2.0%
Other values (4) 5
 
5.0%

Length

2023-12-10T19:15:29.618928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
북미 17
 
5.2%
비핵화 17
 
5.2%
협상 17
 
5.2%
트럼프 17
 
5.2%
대통령 17
 
5.2%
방한 17
 
5.2%
17
 
5.2%
정상회담 17
 
5.2%
버닝썬 17
 
5.2%
사건 17
 
5.2%
Other values (31) 156
47.9%

all_total_co
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.38
Minimum6
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:29.822321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.95
Q120.25
median42
Q353.25
95-th percentile65.05
Maximum72
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.55474
Coefficient of variation (CV)0.48344816
Kurtosis-1.2636001
Mean38.38
Median Absolute Deviation (MAD)16
Skewness-0.13697283
Sum3838
Variance344.27838
MonotonicityNot monotonic
2023-12-10T19:15:30.049243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 5
 
5.0%
56 5
 
5.0%
59 4
 
4.0%
14 4
 
4.0%
12 4
 
4.0%
49 4
 
4.0%
25 3
 
3.0%
50 3
 
3.0%
41 3
 
3.0%
54 3
 
3.0%
Other values (40) 62
62.0%
ValueCountFrequency (%)
6 1
 
1.0%
8 2
2.0%
9 2
2.0%
10 1
 
1.0%
12 4
4.0%
13 1
 
1.0%
14 4
4.0%
15 2
2.0%
16 1
 
1.0%
17 2
2.0%
ValueCountFrequency (%)
72 1
 
1.0%
71 1
 
1.0%
67 1
 
1.0%
66 2
2.0%
65 3
3.0%
64 1
 
1.0%
63 1
 
1.0%
62 1
 
1.0%
59 4
4.0%
58 2
2.0%

male_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.942
Minimum4.5
Maximum75.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:30.257866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile10.4
Q120.45
median44.6
Q356.6
95-th percentile67.24
Maximum75.2
Range70.7
Interquartile range (IQR)36.15

Descriptive statistics

Standard deviation19.739486
Coefficient of variation (CV)0.49420375
Kurtosis-1.3367625
Mean39.942
Median Absolute Deviation (MAD)15.8
Skewness-0.18025338
Sum3994.2
Variance389.64731
MonotonicityNot monotonic
2023-12-10T19:15:30.456534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.0 3
 
3.0%
14.8 3
 
3.0%
60.4 2
 
2.0%
10.4 2
 
2.0%
33.0 2
 
2.0%
61.0 2
 
2.0%
25.2 2
 
2.0%
12.8 2
 
2.0%
30.0 2
 
2.0%
40.4 2
 
2.0%
Other values (71) 78
78.0%
ValueCountFrequency (%)
4.5 1
 
1.0%
9.2 1
 
1.0%
9.5 1
 
1.0%
10.0 1
 
1.0%
10.4 2
2.0%
11.0 3
3.0%
12.0 1
 
1.0%
12.5 1
 
1.0%
12.8 2
2.0%
13.2 1
 
1.0%
ValueCountFrequency (%)
75.2 1
1.0%
73.0 1
1.0%
71.6 1
1.0%
69.6 1
1.0%
68.0 1
1.0%
67.2 1
1.0%
66.0 1
1.0%
65.6 1
1.0%
65.2 1
1.0%
63.2 1
1.0%

female_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.825
Minimum5
Maximum71.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:30.711474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.97
Q120.375
median38.8
Q351.3
95-th percentile64.04
Maximum71.2
Range66.2
Interquartile range (IQR)30.925

Descriptive statistics

Standard deviation17.973675
Coefficient of variation (CV)0.48808351
Kurtosis-1.1007774
Mean36.825
Median Absolute Deviation (MAD)15.5
Skewness-0.07105903
Sum3682.5
Variance323.05301
MonotonicityNot monotonic
2023-12-10T19:15:30.907794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.2 3
 
3.0%
48.0 3
 
3.0%
40.8 3
 
3.0%
52.0 3
 
3.0%
56.0 2
 
2.0%
31.6 2
 
2.0%
37.5 2
 
2.0%
19.2 2
 
2.0%
36.8 2
 
2.0%
51.2 2
 
2.0%
Other values (70) 76
76.0%
ValueCountFrequency (%)
5.0 1
1.0%
6.5 2
2.0%
8.0 1
1.0%
8.4 1
1.0%
9.0 1
1.0%
9.2 1
1.0%
10.4 1
1.0%
11.0 1
1.0%
11.2 1
1.0%
11.5 1
1.0%
ValueCountFrequency (%)
71.2 1
1.0%
68.8 1
1.0%
68.4 1
1.0%
65.6 1
1.0%
64.8 1
1.0%
64.0 2
2.0%
62.0 1
1.0%
60.5 1
1.0%
60.4 1
1.0%
60.0 1
1.0%

all_n10s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.084
Minimum6
Maximum70.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:31.121212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10.38
Q120
median38
Q349.6
95-th percentile62.14
Maximum70.4
Range64.4
Interquartile range (IQR)29.6

Descriptive statistics

Standard deviation17.564983
Coefficient of variation (CV)0.48678038
Kurtosis-1.1663017
Mean36.084
Median Absolute Deviation (MAD)14.8
Skewness-0.0072692835
Sum3608.4
Variance308.52863
MonotonicityNot monotonic
2023-12-10T19:15:31.290942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.8 4
 
4.0%
56.0 4
 
4.0%
40.0 3
 
3.0%
42.4 3
 
3.0%
20.0 3
 
3.0%
61.6 3
 
3.0%
12.8 3
 
3.0%
43.2 3
 
3.0%
44.8 2
 
2.0%
13.6 2
 
2.0%
Other values (57) 70
70.0%
ValueCountFrequency (%)
6.0 2
2.0%
6.4 1
 
1.0%
8.0 1
 
1.0%
10.0 1
 
1.0%
10.4 1
 
1.0%
11.2 1
 
1.0%
12.0 2
2.0%
12.8 3
3.0%
13.6 2
2.0%
14.0 2
2.0%
ValueCountFrequency (%)
70.4 1
 
1.0%
67.2 1
 
1.0%
65.0 1
 
1.0%
64.8 2
2.0%
62.0 1
 
1.0%
61.6 3
3.0%
60.8 1
 
1.0%
60.0 1
 
1.0%
58.4 1
 
1.0%
57.6 1
 
1.0%

all_n20s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.598
Minimum3
Maximum73.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:31.498998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q120
median40
Q351.2
95-th percentile60.96
Maximum73.6
Range70.6
Interquartile range (IQR)31.2

Descriptive statistics

Standard deviation17.779582
Coefficient of variation (CV)0.47288637
Kurtosis-1.0810322
Mean37.598
Median Absolute Deviation (MAD)14.4
Skewness-0.095268529
Sum3759.8
Variance316.11353
MonotonicityNot monotonic
2023-12-10T19:15:31.713731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.4 4
 
4.0%
51.2 4
 
4.0%
40.0 4
 
4.0%
56.0 3
 
3.0%
46.4 3
 
3.0%
49.6 3
 
3.0%
10.0 3
 
3.0%
20.0 3
 
3.0%
44.8 3
 
3.0%
26.4 3
 
3.0%
Other values (53) 67
67.0%
ValueCountFrequency (%)
3.0 1
 
1.0%
8.8 1
 
1.0%
9.0 1
 
1.0%
9.6 1
 
1.0%
10.0 3
3.0%
10.4 1
 
1.0%
11.2 1
 
1.0%
12.8 1
 
1.0%
13.0 1
 
1.0%
13.6 2
2.0%
ValueCountFrequency (%)
73.6 1
 
1.0%
72.0 1
 
1.0%
71.0 1
 
1.0%
66.0 1
 
1.0%
64.0 1
 
1.0%
60.8 2
2.0%
60.0 2
2.0%
59.2 1
 
1.0%
58.4 4
4.0%
57.6 2
2.0%

all_n30s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.102
Minimum6
Maximum75.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:32.365964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.9
Q120.6
median42.4
Q355.25
95-th percentile67.24
Maximum75.2
Range69.2
Interquartile range (IQR)34.65

Descriptive statistics

Standard deviation19.002169
Coefficient of variation (CV)0.48596412
Kurtosis-1.2077201
Mean39.102
Median Absolute Deviation (MAD)15.6
Skewness-0.068077061
Sum3910.2
Variance361.08242
MonotonicityNot monotonic
2023-12-10T19:15:32.657833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.2 5
 
5.0%
12.0 4
 
4.0%
48.0 4
 
4.0%
16.0 3
 
3.0%
59.2 3
 
3.0%
28.8 3
 
3.0%
60.8 3
 
3.0%
42.4 3
 
3.0%
20.0 2
 
2.0%
15.0 2
 
2.0%
Other values (57) 68
68.0%
ValueCountFrequency (%)
6.0 1
 
1.0%
7.0 1
 
1.0%
7.2 1
 
1.0%
8.0 1
 
1.0%
10.0 1
 
1.0%
12.0 4
4.0%
13.6 1
 
1.0%
14.0 1
 
1.0%
14.4 2
2.0%
15.0 2
2.0%
ValueCountFrequency (%)
75.2 1
1.0%
72.0 1
1.0%
70.0 1
1.0%
69.6 1
1.0%
68.0 1
1.0%
67.2 1
1.0%
66.4 2
2.0%
65.0 1
1.0%
64.8 1
1.0%
63.2 1
1.0%

all_n40s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.997
Minimum0
Maximum77.9
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:32.979733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.345
Q118.525
median45.3
Q357.5
95-th percentile68.75
Maximum77.9
Range77.9
Interquartile range (IQR)38.975

Descriptive statistics

Standard deviation20.959603
Coefficient of variation (CV)0.53746705
Kurtosis-1.360309
Mean38.997
Median Absolute Deviation (MAD)17.7
Skewness-0.1115678
Sum3899.7
Variance439.30494
MonotonicityNot monotonic
2023-12-10T19:15:33.252806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.1 2
 
2.0%
17.4 2
 
2.0%
25.0 2
 
2.0%
17.5 2
 
2.0%
51.7 2
 
2.0%
25.3 2
 
2.0%
63.0 2
 
2.0%
65.4 1
 
1.0%
60.3 1
 
1.0%
22.2 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 1
1.0%
3.8 1
1.0%
5.2 1
1.0%
5.6 1
1.0%
6.3 1
1.0%
7.4 1
1.0%
7.7 1
1.0%
9.1 1
1.0%
10.3 1
1.0%
10.4 1
1.0%
ValueCountFrequency (%)
77.9 1
1.0%
74.0 1
1.0%
71.7 1
1.0%
71.0 1
1.0%
69.7 1
1.0%
68.7 1
1.0%
66.7 1
1.0%
65.4 1
1.0%
65.2 1
1.0%
63.6 1
1.0%

all_n50s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.225
Minimum0
Maximum88.1
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:33.605787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.695
Q115.7
median52
Q366.55
95-th percentile79.715
Maximum88.1
Range88.1
Interquartile range (IQR)50.85

Descriptive statistics

Standard deviation26.628148
Coefficient of variation (CV)0.61603581
Kurtosis-1.4157513
Mean43.225
Median Absolute Deviation (MAD)20.95
Skewness-0.18372159
Sum4322.5
Variance709.05826
MonotonicityNot monotonic
2023-12-10T19:15:33.848212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.4 4
 
4.0%
52.6 3
 
3.0%
18.8 3
 
3.0%
14.3 2
 
2.0%
7.1 2
 
2.0%
74.0 2
 
2.0%
8.7 2
 
2.0%
65.9 2
 
2.0%
52.0 2
 
2.0%
58.8 2
 
2.0%
Other values (73) 76
76.0%
ValueCountFrequency (%)
0.0 1
1.0%
1.4 1
1.0%
2.2 1
1.0%
2.4 1
1.0%
2.6 1
1.0%
2.7 1
1.0%
3.6 1
1.0%
3.9 1
1.0%
4.3 1
1.0%
7.1 2
2.0%
ValueCountFrequency (%)
88.1 1
1.0%
86.4 1
1.0%
83.3 1
1.0%
80.8 1
1.0%
80.0 1
1.0%
79.7 1
1.0%
78.1 1
1.0%
76.9 1
1.0%
75.0 2
2.0%
74.6 1
1.0%

reprt_year_cn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
84 
2019
13 
2018
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2018
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 84
84.0%
2019 13
 
13.0%
2018 3
 
3.0%

Length

2023-12-10T19:15:34.081406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:34.393281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 84
84.0%
2019 13
 
13.0%
2018 3
 
3.0%

examin_country_nm
Categorical

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중국
10 
일본
10 
대만
8 
남아공
8 
베트남
 
5
Other values (12)
59 

Length

Max length5
Median length2
Mean length2.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중국 10
 
10.0%
일본 10
 
10.0%
대만 8
 
8.0%
남아공 8
 
8.0%
베트남 5
 
5.0%
UAE 5
 
5.0%
태국 5
 
5.0%
말레이시아 5
 
5.0%
인도 5
 
5.0%
호주 5
 
5.0%
Other values (7) 34
34.0%

Length

2023-12-10T19:15:34.614035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 10
 
10.0%
일본 10
 
10.0%
대만 8
 
8.0%
남아공 8
 
8.0%
프랑스 5
 
5.0%
미국 5
 
5.0%
터키 5
 
5.0%
러시아 5
 
5.0%
영국 5
 
5.0%
브라질 5
 
5.0%
Other values (7) 34
34.0%

Interactions

2023-12-10T19:15:27.847633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:18.426619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.530087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.556172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.576512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:22.748474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:25.255130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:26.478588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:27.989523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:18.564044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.659234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.682029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.708559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:22.876637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:25.382605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:26.640182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:28.141658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:18.690149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.781188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.796986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.844299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:23.015398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:25.523617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:26.826706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:28.288248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:18.829772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.907914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.917216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.991904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:23.262300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:25.683432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:27.005882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:28.427863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:18.966072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.027456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.048380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:22.143245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:24.479676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:25.842880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:27.164572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:28.585158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.121476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.150055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.197340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:22.283003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:24.757623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:26.027062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:27.336241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:28.755838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.248049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.286026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.323824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:22.426277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:24.902021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:26.193844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:27.508575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:28.912087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:19.396155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:20.434072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:21.458507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:22.601308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:25.108480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:26.349557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:27.688967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:15:34.784208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
issue_cnall_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratereprt_year_cnexamin_country_nm
issue_cn1.0000.6450.7500.6550.6350.6930.6330.6390.7811.0000.000
all_total_co0.6451.0000.9410.9440.8440.9280.9520.9070.8760.3590.000
male_rate0.7500.9411.0000.8590.8150.9030.9300.9270.8830.5090.223
female_rate0.6550.9440.8591.0000.8780.9030.8810.8450.8720.4570.000
all_n10s_rate0.6350.8440.8150.8781.0000.8370.7890.7650.8090.4320.000
all_n20s_rate0.6930.9280.9030.9030.8371.0000.8960.7970.7470.4040.000
all_n30s_rate0.6330.9520.9300.8810.7890.8961.0000.8860.8530.4630.090
all_n40s_rate0.6390.9070.9270.8450.7650.7970.8861.0000.8240.4180.000
all_n50s_rate0.7810.8760.8830.8720.8090.7470.8530.8241.0000.5260.263
reprt_year_cn1.0000.3590.5090.4570.4320.4040.4630.4180.5261.0000.696
examin_country_nm0.0000.0000.2230.0000.0000.0000.0900.0000.2630.6961.000
2023-12-10T19:15:35.008802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
examin_country_nmissue_cnreprt_year_cn
examin_country_nm1.0000.0000.462
issue_cn0.0001.0000.942
reprt_year_cn0.4620.9421.000
2023-12-10T19:15:35.183397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
all_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_rateissue_cnreprt_year_cnexamin_country_nm
all_total_co1.0000.9800.9800.9420.9570.9730.9460.9040.3160.2210.000
male_rate0.9801.0000.9280.9060.9290.9640.9380.9160.4130.3410.074
female_rate0.9800.9281.0000.9440.9570.9500.9210.8600.3240.2960.000
all_n10s_rate0.9420.9060.9441.0000.8990.8720.8530.8140.3080.2760.000
all_n20s_rate0.9570.9290.9570.8991.0000.9380.8720.8000.3570.2540.000
all_n30s_rate0.9730.9640.9500.8720.9381.0000.9200.8820.3070.3020.000
all_n40s_rate0.9460.9380.9210.8530.8720.9201.0000.9110.3120.2650.000
all_n50s_rate0.9040.9160.8600.8140.8000.8820.9111.0000.4480.3560.092
issue_cn0.3160.4130.3240.3080.3570.3070.3120.4481.0000.9420.000
reprt_year_cn0.2210.3410.2960.2760.2540.3020.2650.3560.9421.0000.462
examin_country_nm0.0000.0740.0000.0000.0000.0000.0000.0920.0000.4621.000

Missing values

2023-12-10T19:15:29.098028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:15:29.374797image/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

issue_cnall_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratereprt_year_cnexamin_country_nm
0북미 한반도 비핵화 협상5860.456.048.856.064.865.452.42020중국
1촛불집회2526.024.019.024.024.026.341.92018남아공
2트럼프 대통령 방한 + 정상회담4346.439.242.446.436.848.133.32020중국
3버닝썬 사건3938.839.242.449.636.030.814.32020중국
4지소미아(GSOMIA) 파기 결정2524.425.620.828.026.426.914.32020중국
5위의 보기 중 전혀 없음1212.012.420.08.88.012.59.52020중국
6일본-한국 무역갈등6668.064.061.660.068.071.776.92020일본
7한국의사드(THAAD)배치1824.511.014.020.019.017.518.62018남아공
8북미 한반도 비핵화 협상5663.248.052.053.659.251.763.12020일본
9트럼프 대통령 방한 + 정상회담5257.247.648.849.652.051.766.22020일본
issue_cnall_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratereprt_year_cnexamin_country_nm
90북미정상회담을 통한 미국과 북한의 화해무드4751.642.434.448.054.449.060.02019중국
91남북정상회담에 따른 남북 화해무드4653.637.631.240.848.060.072.02019중국
92북한의 핵∙미사일 위협7275.268.470.472.067.269.786.42019일본
93위안부 문제로 인한 한일갈등6565.265.664.864.060.865.279.72019일본
942018 평창 동계올림픽6561.268.867.257.666.463.674.62019일본
95남북정상회담에 따른 남북 화해무드4652.439.242.440.042.456.161.02019일본
96북미정상회담을 통한 미국과 북한의 화해무드4545.643.643.242.443.240.959.32019일본
97북한의 핵∙미사일 위협7171.671.261.673.675.268.788.12019대만
98남북정상회담에 따른 남북 화해무드5055.645.237.651.250.457.871.42019대만
99위안부 문제로 인한 한일갈등4640.452.044.851.241.647.047.62019대만