Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 KiB
Average record size in memory114.3 B

Variable types

Categorical5
Numeric8

Alerts

country_rank_nm is highly overall correlated with country_nm and 2 other fieldsHigh correlation
reprt_year_cn is highly overall correlated with country_nm and 3 other fieldsHigh correlation
country_nm is highly overall correlated with country_rank_nm and 2 other fieldsHigh correlation
examin_country_nm is highly overall correlated with reprt_year_cnHigh correlation
cntnts_url is highly overall correlated with country_nm and 2 other fieldsHigh 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 (80.6%)Imbalance
all_n40s_rate has 4 (4.0%) zerosZeros
all_n50s_rate has 8 (8.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:08:47.002145
Analysis finished2023-12-10 10:08:59.653556
Duration12.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

country_nm
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한국
31 
미국
28 
중국
17 
일본
14 
영국
7 
Other values (3)
 
3

Length

Max length3
Median length2
Mean length2.02
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row한국
2nd row브라질
3rd row일본
4th row한국
5th row미국

Common Values

ValueCountFrequency (%)
한국 31
31.0%
미국 28
28.0%
중국 17
17.0%
일본 14
14.0%
영국 7
 
7.0%
브라질 1
 
1.0%
베트남 1
 
1.0%
기타 1
 
1.0%

Length

2023-12-10T19:08:59.812907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:00.108017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국 31
31.0%
미국 28
28.0%
중국 17
17.0%
일본 14
14.0%
영국 7
 
7.0%
브라질 1
 
1.0%
베트남 1
 
1.0%
기타 1
 
1.0%

country_rank_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1순위
34 
3순위
33 
2순위
30 
9순위
 
1
10순위
 
1

Length

Max length4
Median length3
Mean length3.02
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row1순위
2nd row9순위
3rd row3순위
4th row1순위
5th row2순위

Common Values

ValueCountFrequency (%)
1순위 34
34.0%
3순위 33
33.0%
2순위 30
30.0%
9순위 1
 
1.0%
10순위 1
 
1.0%
11순위 1
 
1.0%

Length

2023-12-10T19:09:00.368462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:00.582829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1순위 34
34.0%
3순위 33
33.0%
2순위 30
30.0%
9순위 1
 
1.0%
10순위 1
 
1.0%
11순위 1
 
1.0%

all_total_co
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.01
Minimum3
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:00.824551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q114
median25.5
Q341
95-th percentile71.1
Maximum86
Range83
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.025475
Coefficient of variation (CV)0.70061563
Kurtosis-0.22003685
Mean30.01
Median Absolute Deviation (MAD)13.5
Skewness0.81136109
Sum3001
Variance442.07061
MonotonicityNot monotonic
2023-12-10T19:09:01.062443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6
 
6.0%
29 5
 
5.0%
14 5
 
5.0%
10 4
 
4.0%
17 4
 
4.0%
37 4
 
4.0%
52 3
 
3.0%
36 3
 
3.0%
26 3
 
3.0%
23 3
 
3.0%
Other values (40) 60
60.0%
ValueCountFrequency (%)
3 1
 
1.0%
4 3
3.0%
5 6
6.0%
7 2
 
2.0%
8 2
 
2.0%
9 2
 
2.0%
10 4
4.0%
11 2
 
2.0%
12 2
 
2.0%
14 5
5.0%
ValueCountFrequency (%)
86 1
1.0%
82 1
1.0%
75 1
1.0%
73 2
2.0%
71 2
2.0%
69 1
1.0%
67 1
1.0%
65 1
1.0%
61 1
1.0%
60 2
2.0%

male_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.838
Minimum1.9
Maximum83.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:01.303669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile4.275
Q112.95
median24.75
Q339.35
95-th percentile75.805
Maximum83.6
Range81.7
Interquartile range (IQR)26.4

Descriptive statistics

Standard deviation21.564092
Coefficient of variation (CV)0.72270567
Kurtosis-0.24376464
Mean29.838
Median Absolute Deviation (MAD)12.55
Skewness0.83128455
Sum2983.8
Variance465.01006
MonotonicityNot monotonic
2023-12-10T19:09:02.054711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.6 2
 
2.0%
12.2 2
 
2.0%
23.3 2
 
2.0%
26.3 2
 
2.0%
4.5 2
 
2.0%
7.4 2
 
2.0%
19.8 2
 
2.0%
15.5 2
 
2.0%
52.6 1
 
1.0%
57.1 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
1.9 1
1.0%
2.3 1
1.0%
3.2 1
1.0%
3.3 1
1.0%
3.8 1
1.0%
4.3 1
1.0%
4.5 2
2.0%
4.9 1
1.0%
5.1 1
1.0%
5.8 1
1.0%
ValueCountFrequency (%)
83.6 1
1.0%
83.0 1
1.0%
77.5 1
1.0%
76.7 1
1.0%
75.9 1
1.0%
75.8 1
1.0%
67.9 1
1.0%
65.6 1
1.0%
65.5 1
1.0%
64.8 1
1.0%

female_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.032
Minimum2
Maximum87.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:02.420524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.87
Q112.5
median24.95
Q345.975
95-th percentile71.72
Maximum87.5
Range85.5
Interquartile range (IQR)33.475

Descriptive statistics

Standard deviation21.353101
Coefficient of variation (CV)0.71101162
Kurtosis-0.35384683
Mean30.032
Median Absolute Deviation (MAD)15.6
Skewness0.74109678
Sum3003.2
Variance455.95493
MonotonicityNot monotonic
2023-12-10T19:09:02.700572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.0 2
 
2.0%
14.0 2
 
2.0%
47.4 2
 
2.0%
12.9 2
 
2.0%
26.6 2
 
2.0%
9.9 2
 
2.0%
5.9 2
 
2.0%
42.3 1
 
1.0%
22.3 1
 
1.0%
57.4 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
2.0 1
1.0%
2.1 1
1.0%
3.2 1
1.0%
3.5 1
1.0%
4.3 1
1.0%
4.9 1
1.0%
5.2 1
1.0%
5.9 2
2.0%
6.0 1
1.0%
6.4 1
1.0%
ValueCountFrequency (%)
87.5 1
1.0%
80.7 1
1.0%
76.2 1
1.0%
73.2 1
1.0%
72.1 1
1.0%
71.7 1
1.0%
69.3 1
1.0%
68.6 1
1.0%
67.0 1
1.0%
66.3 1
1.0%

all_n10s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.3
Minimum0
Maximum74.1
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:02.967788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.095
Q112.075
median24.25
Q341.85
95-th percentile67.495
Maximum74.1
Range74.1
Interquartile range (IQR)29.775

Descriptive statistics

Standard deviation20.632327
Coefficient of variation (CV)0.70417499
Kurtosis-0.59401961
Mean29.3
Median Absolute Deviation (MAD)13.95
Skewness0.71810472
Sum2930
Variance425.69293
MonotonicityNot monotonic
2023-12-10T19:09:03.263330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.2 3
 
3.0%
65.2 2
 
2.0%
30.9 2
 
2.0%
5.0 2
 
2.0%
10.0 2
 
2.0%
67.3 2
 
2.0%
74.1 2
 
2.0%
37.3 1
 
1.0%
35.0 1
 
1.0%
64.0 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 1
1.0%
3.8 1
1.0%
4.3 1
1.0%
5.0 2
2.0%
5.1 1
1.0%
5.8 1
1.0%
6.0 1
1.0%
6.3 1
1.0%
6.6 1
1.0%
6.8 1
1.0%
ValueCountFrequency (%)
74.1 2
2.0%
73.7 1
1.0%
73.0 1
1.0%
71.2 1
1.0%
67.3 2
2.0%
66.7 1
1.0%
65.2 2
2.0%
64.0 1
1.0%
62.3 1
1.0%
60.0 1
1.0%

all_n20s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.298
Minimum1.1
Maximum86.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:03.537569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.995
Q114.825
median23.8
Q344.025
95-th percentile73.125
Maximum86.7
Range85.6
Interquartile range (IQR)29.2

Descriptive statistics

Standard deviation21.350386
Coefficient of variation (CV)0.70467972
Kurtosis-0.27095767
Mean30.298
Median Absolute Deviation (MAD)13.6
Skewness0.75449854
Sum3029.8
Variance455.83899
MonotonicityNot monotonic
2023-12-10T19:09:03.787289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.8 3
 
3.0%
8.0 2
 
2.0%
62.0 2
 
2.0%
41.9 2
 
2.0%
21.0 2
 
2.0%
20.8 2
 
2.0%
35.8 2
 
2.0%
55.6 1
 
1.0%
17.1 1
 
1.0%
28.2 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
1.1 1
1.0%
1.2 1
1.0%
2.1 1
1.0%
2.7 1
1.0%
2.9 1
1.0%
3.0 1
1.0%
3.8 1
1.0%
4.2 1
1.0%
5.0 1
1.0%
5.2 1
1.0%
ValueCountFrequency (%)
86.7 1
1.0%
82.2 1
1.0%
79.0 1
1.0%
74.7 1
1.0%
73.6 1
1.0%
73.1 1
1.0%
71.3 1
1.0%
65.7 1
1.0%
62.0 2
2.0%
61.6 1
1.0%

all_n30s_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.29
Minimum0
Maximum88
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:04.044003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111.875
median27.3
Q344.175
95-th percentile73.83
Maximum88
Range88
Interquartile range (IQR)32.3

Descriptive statistics

Standard deviation22.289389
Coefficient of variation (CV)0.73586626
Kurtosis-0.17955042
Mean30.29
Median Absolute Deviation (MAD)16
Skewness0.78091319
Sum3029
Variance496.81687
MonotonicityNot monotonic
2023-12-10T19:09:04.308126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 4
 
4.0%
2.3 2
 
2.0%
50.0 2
 
2.0%
7.0 2
 
2.0%
31.9 2
 
2.0%
13.8 2
 
2.0%
43.2 1
 
1.0%
18.9 1
 
1.0%
31.1 1
 
1.0%
47.5 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
2.3 2
2.0%
2.4 1
 
1.0%
4.0 4
4.0%
4.2 1
 
1.0%
4.3 1
 
1.0%
5.6 1
 
1.0%
6.7 1
 
1.0%
7.0 2
2.0%
7.4 1
 
1.0%
ValueCountFrequency (%)
88.0 1
1.0%
84.0 1
1.0%
81.6 1
1.0%
81.3 1
1.0%
78.2 1
1.0%
73.6 1
1.0%
73.2 1
1.0%
71.4 1
1.0%
71.2 1
1.0%
66.2 1
1.0%

all_n40s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.861
Minimum0
Maximum90.7
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:04.555890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q111.05
median23.65
Q343.5
95-th percentile71.175
Maximum90.7
Range90.7
Interquartile range (IQR)32.45

Descriptive statistics

Standard deviation22.697964
Coefficient of variation (CV)0.76012068
Kurtosis-0.25949113
Mean29.861
Median Absolute Deviation (MAD)14.15
Skewness0.81546873
Sum2986.1
Variance515.19755
MonotonicityNot monotonic
2023-12-10T19:09:04.823562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
4.3 2
 
2.0%
23.9 2
 
2.0%
24.1 2
 
2.0%
2.6 2
 
2.0%
60.9 2
 
2.0%
17.9 2
 
2.0%
36.3 2
 
2.0%
22.2 2
 
2.0%
25.0 2
 
2.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 4
4.0%
2.6 2
2.0%
4.3 2
2.0%
5.1 1
 
1.0%
5.6 1
 
1.0%
6.1 1
 
1.0%
6.5 1
 
1.0%
6.8 1
 
1.0%
7.0 1
 
1.0%
7.4 1
 
1.0%
ValueCountFrequency (%)
90.7 1
1.0%
84.8 1
1.0%
83.3 1
1.0%
76.9 1
1.0%
74.5 1
1.0%
71.0 1
1.0%
70.8 1
1.0%
69.6 1
1.0%
67.7 1
1.0%
65.8 1
1.0%

all_n50s_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.795
Minimum0
Maximum97.1
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:05.079573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.1
median22.2
Q345.675
95-th percentile75.14
Maximum97.1
Range97.1
Interquartile range (IQR)36.575

Descriptive statistics

Standard deviation25.028958
Coefficient of variation (CV)0.84003888
Kurtosis-0.43111936
Mean29.795
Median Absolute Deviation (MAD)15.1
Skewness0.81226043
Sum2979.5
Variance626.44876
MonotonicityNot monotonic
2023-12-10T19:09:05.336122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
20.0 5
 
5.0%
11.1 4
 
4.0%
63.2 2
 
2.0%
14.3 2
 
2.0%
13.6 2
 
2.0%
60.0 2
 
2.0%
5.9 2
 
2.0%
7.4 2
 
2.0%
36.7 2
 
2.0%
Other values (60) 69
69.0%
ValueCountFrequency (%)
0.0 8
8.0%
2.9 1
 
1.0%
3.3 1
 
1.0%
3.7 1
 
1.0%
4.2 1
 
1.0%
5.0 1
 
1.0%
5.9 2
 
2.0%
6.3 2
 
2.0%
6.7 2
 
2.0%
7.1 2
 
2.0%
ValueCountFrequency (%)
97.1 1
1.0%
87.5 1
1.0%
81.8 1
1.0%
80.0 1
1.0%
77.8 1
1.0%
75.0 1
1.0%
72.7 1
1.0%
71.4 2
2.0%
70.8 1
1.0%
70.6 1
1.0%

examin_country_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일본
9 
대만
9 
말레이시아
9 
인도네시아
9 
인도
9 
Other values (8)
55 

Length

Max length5
Median length2
Mean length2.76
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
일본 9
9.0%
대만 9
9.0%
말레이시아 9
9.0%
인도네시아 9
9.0%
인도 9
9.0%
베트남 9
9.0%
호주 9
9.0%
미국 9
9.0%
브라질 9
9.0%
태국 8
8.0%
Other values (3) 11
11.0%

Length

2023-12-10T19:09:05.654815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일본 9
9.0%
대만 9
9.0%
말레이시아 9
9.0%
인도네시아 9
9.0%
인도 9
9.0%
베트남 9
9.0%
호주 9
9.0%
미국 9
9.0%
브라질 9
9.0%
태국 8
8.0%
Other values (3) 11
11.0%

reprt_year_cn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
97 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2021
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 97
97.0%
2021 3
 
3.0%

Length

2023-12-10T19:09:06.034099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:06.198162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 97
97.0%
2021 3
 
3.0%

cntnts_url
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
드라마
33 
예능
32 
영화
32 
음식
 
3

Length

Max length3
Median length2
Mean length2.33
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row드라마
2nd row음식
3rd row드라마
4th row예능
5th row예능

Common Values

ValueCountFrequency (%)
드라마 33
33.0%
예능 32
32.0%
영화 32
32.0%
음식 3
 
3.0%

Length

2023-12-10T19:09:06.395955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:09:06.569907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
드라마 33
33.0%
예능 32
32.0%
영화 32
32.0%
음식 3
 
3.0%

Interactions

2023-12-10T19:08:57.855616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:48.257022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:49.513172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:51.104421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:53.033822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.398572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.542731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.754754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.988487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:48.526775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:49.672678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:51.757398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:53.225486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.521138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.690331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.887203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:58.162385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:48.707153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:49.855422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:52.026742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:53.396873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.685689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.869833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.048072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:58.290508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:48.830629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:50.003471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:52.168654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:53.553344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.830229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.039475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.184154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:58.451155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:48.993365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:50.171084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:52.338329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:53.759860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.987185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.201621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.344003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:58.599071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:49.130780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:50.386303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:52.527816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:53.909228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.118813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.346033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.475402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:58.736594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:49.270581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:50.618214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:52.658868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.068903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.260962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.478612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.603654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:58.879186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:49.387463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:50.824208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:52.875862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:54.249913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.398847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:56.618037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:57.731003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:09:06.740386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
country_nmcountry_rank_nmall_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_rateexamin_country_nmreprt_year_cncntnts_url
country_nm1.0000.9400.4010.4930.4020.5190.4800.3360.3990.4270.6281.0000.860
country_rank_nm0.9401.0000.6180.5810.6110.6180.6400.5700.5800.5220.5541.0000.714
all_total_co0.4010.6181.0000.9470.9650.8860.9500.9490.9410.8560.5240.1920.258
male_rate0.4930.5810.9471.0000.8910.9070.8970.9240.9050.8520.7020.3670.339
female_rate0.4020.6110.9650.8911.0000.8670.9320.9460.9140.8320.3680.1920.107
all_n10s_rate0.5190.6180.8860.9070.8671.0000.8450.8700.8380.8020.1270.4660.481
all_n20s_rate0.4800.6400.9500.8970.9320.8451.0000.9030.8600.7610.4440.0000.000
all_n30s_rate0.3360.5700.9490.9240.9460.8700.9031.0000.8680.7970.6140.2800.327
all_n40s_rate0.3990.5800.9410.9050.9140.8380.8600.8681.0000.8290.4680.2510.358
all_n50s_rate0.4270.5220.8560.8520.8320.8020.7610.7970.8291.0000.4300.0000.000
examin_country_nm0.6280.5540.5240.7020.3680.1270.4440.6140.4680.4301.0001.0000.708
reprt_year_cn1.0001.0000.1920.3670.1920.4660.0000.2800.2510.0001.0001.0001.000
cntnts_url0.8600.7140.2580.3390.1070.4810.0000.3270.3580.0000.7081.0001.000
2023-12-10T19:09:07.014042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
country_rank_nmreprt_year_cncountry_nmexamin_country_nmcntnts_url
country_rank_nm1.0000.9790.8470.3000.540
reprt_year_cn0.9791.0000.9690.9420.990
country_nm0.8470.9691.0000.3340.526
examin_country_nm0.3000.9420.3341.0000.477
cntnts_url0.5400.9900.5260.4771.000
2023-12-10T19:09:07.233394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
all_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_ratecountry_nmcountry_rank_nmexamin_country_nmreprt_year_cncntnts_url
all_total_co1.0000.9740.9790.9430.9650.9740.9350.8970.2000.3760.2410.1380.149
male_rate0.9741.0000.9140.9300.9250.9380.9280.9000.2570.3450.3760.2690.200
female_rate0.9790.9141.0000.9110.9600.9650.9080.8590.2000.3690.1540.1380.054
all_n10s_rate0.9430.9300.9111.0000.8710.8950.8630.8390.2750.3760.0360.3420.296
all_n20s_rate0.9650.9250.9600.8711.0000.9320.8740.8430.2490.3960.1940.0000.000
all_n30s_rate0.9740.9380.9650.8950.9321.0000.8930.8400.1610.3340.3110.2030.216
all_n40s_rate0.9350.9280.9080.8630.8740.8931.0000.8670.1980.3440.2080.1820.212
all_n50s_rate0.8970.9000.8590.8390.8430.8400.8671.0000.2150.2990.1870.0000.000
country_nm0.2000.2570.2000.2750.2490.1610.1980.2151.0000.8470.3340.9690.526
country_rank_nm0.3760.3450.3690.3760.3960.3340.3440.2990.8471.0000.3000.9790.540
examin_country_nm0.2410.3760.1540.0360.1940.3110.2080.1870.3340.3001.0000.9420.477
reprt_year_cn0.1380.2690.1380.3420.0000.2030.1820.0000.9690.9790.9421.0000.990
cntnts_url0.1490.2000.0540.2960.0000.2160.2120.0000.5260.5400.4770.9901.000

Missing values

2023-12-10T19:08:59.167457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:59.506816image/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

country_nmcountry_rank_nmall_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_rateexamin_country_nmreprt_year_cncntnts_url
0한국1순위4139.143.037.341.343.237.863.2중국2020드라마
1브라질9순위55.83.25.012.52.30.00.0남아공2021음식
2일본3순위1515.514.020.014.016.810.20.0중국2020드라마
3한국1순위4742.951.444.754.148.634.968.8중국2020예능
4미국2순위2729.625.030.922.027.531.318.8중국2020예능
5일본3순위1921.216.320.217.417.424.10.0중국2020예능
6미국1순위6062.157.366.762.058.257.525.0중국2020영화
7베트남10순위44.92.10.04.27.02.63.7남아공2021음식
8일본3순위1110.112.612.29.311.813.86.3중국2020영화
9미국1순위7377.568.659.573.681.676.975.0일본2020드라마
country_nmcountry_rank_nmall_total_comale_ratefemale_rateall_n10s_rateall_n20s_rateall_n30s_rateall_n40s_rateall_n50s_rateexamin_country_nmreprt_year_cncntnts_url
90미국1순위7376.769.362.379.073.274.572.7브라질2020드라마
91한국2순위1715.517.621.318.515.510.613.6브라질2020드라마
92일본3순위42.35.26.61.25.64.30.0브라질2020드라마
93미국1순위7575.973.273.774.771.483.371.4브라질2020예능
94한국2순위1415.012.210.516.515.78.314.3브라질2020예능
95중국3순위54.54.98.83.84.30.07.1브라질2020예능
96미국1순위8283.080.773.082.284.090.780.0브라질2020영화
97한국2순위97.49.910.89.97.07.46.7브라질2020영화
98중국3순위53.26.412.23.04.00.03.3브라질2020영화
99미국1순위6767.967.074.160.666.267.781.8프랑스2020드라마