Overview

Dataset statistics

Number of variables16
Number of observations419
Missing cells322
Missing cells (%)4.8%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory57.8 KiB
Average record size in memory141.3 B

Variable types

Categorical2
Text1
Numeric13

Dataset

Description국내체류 외국국적동포에 대한 정부정책수립의 기초자료로 활용할 수 있으며, 동포에 대한 영주자격 부여 등 외국인정책 수립의 기초자료로 활용할수 있도록 자료를 제공(국내에 체류하는 연령/국가별 체류 외국인 현황)
Author법무부
URLhttps://www.data.go.kr/data/3075821/fileData.do

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
0세_4세 is highly overall correlated with 5세_9세 and 11 other fieldsHigh correlation
5세_9세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
10세_14세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
15세_19세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
20세_24세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
25세_29세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
30세_34세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
35세_39세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
40세_44세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
45세_49세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
50세_54세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
55세_59세 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
60세이상 is highly overall correlated with 0세_4세 and 11 other fieldsHigh correlation
국적 has 23 (5.5%) missing valuesMissing
0세_4세 has 23 (5.5%) missing valuesMissing
5세_9세 has 23 (5.5%) missing valuesMissing
10세_14세 has 23 (5.5%) missing valuesMissing
15세_19세 has 23 (5.5%) missing valuesMissing
20세_24세 has 23 (5.5%) missing valuesMissing
25세_29세 has 23 (5.5%) missing valuesMissing
30세_34세 has 23 (5.5%) missing valuesMissing
35세_39세 has 23 (5.5%) missing valuesMissing
40세_44세 has 23 (5.5%) missing valuesMissing
45세_49세 has 23 (5.5%) missing valuesMissing
50세_54세 has 23 (5.5%) missing valuesMissing
55세_59세 has 23 (5.5%) missing valuesMissing
60세이상 has 23 (5.5%) missing valuesMissing
0세_4세 has 140 (33.4%) zerosZeros
5세_9세 has 133 (31.7%) zerosZeros
10세_14세 has 152 (36.3%) zerosZeros
15세_19세 has 128 (30.5%) zerosZeros
20세_24세 has 62 (14.8%) zerosZeros
25세_29세 has 55 (13.1%) zerosZeros
30세_34세 has 55 (13.1%) zerosZeros
35세_39세 has 66 (15.8%) zerosZeros
40세_44세 has 78 (18.6%) zerosZeros
45세_49세 has 89 (21.2%) zerosZeros
50세_54세 has 112 (26.7%) zerosZeros
55세_59세 has 113 (27.0%) zerosZeros
60세이상 has 96 (22.9%) zerosZeros

Reproduction

Analysis started2024-03-15 01:36:32.798339
Analysis finished2024-03-15 01:37:17.299196
Duration44.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대륙
Categorical

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
아프리카주
107 
유럽주
99 
아시아주
95 
남아메리카주
65 
오세아니아주
23 
Other values (3)
30 

Length

Max length6
Median length5
Mean length4.4534606
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아프리카주 107
25.5%
유럽주 99
23.6%
아시아주 95
22.7%
남아메리카주 65
15.5%
오세아니아주 23
 
5.5%
<NA> 23
 
5.5%
북아메리카주 5
 
1.2%
기타 2
 
0.5%

Length

2024-03-15T10:37:17.557475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:37:18.105515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아프리카주 107
25.5%
유럽주 99
23.6%
아시아주 95
22.7%
남아메리카주 65
15.5%
오세아니아주 23
 
5.5%
na 23
 
5.5%
북아메리카주 5
 
1.2%
기타 2
 
0.5%

국적
Text

MISSING 

Distinct203
Distinct (%)51.3%
Missing23
Missing (%)5.5%
Memory size3.4 KiB
2024-03-15T10:37:19.231606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.0252525
Min length2

Characters and Unicode

Total characters1594
Distinct characters187
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)2.8%

Sample

1st row한국계중국인
2nd row한국계중국인
3rd row중국
4th row중국
5th row베트남
ValueCountFrequency (%)
미국 3
 
0.8%
가봉 2
 
0.5%
마샬군도 2
 
0.5%
핀란드 2
 
0.5%
파푸아뉴기니 2
 
0.5%
보스니아-헤르체고비나 2
 
0.5%
코소보 2
 
0.5%
리히텐슈타인 2
 
0.5%
산마리노 2
 
0.5%
오스트레일리아 2
 
0.5%
Other values (193) 375
94.7%
2024-03-15T10:37:20.502914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
6.8%
62
 
3.9%
59
 
3.7%
49
 
3.1%
48
 
3.0%
46
 
2.9%
38
 
2.4%
30
 
1.9%
29
 
1.8%
29
 
1.8%
Other values (177) 1095
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1588
99.6%
Dash Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
109
 
6.9%
62
 
3.9%
59
 
3.7%
49
 
3.1%
48
 
3.0%
46
 
2.9%
38
 
2.4%
30
 
1.9%
29
 
1.8%
29
 
1.8%
Other values (174) 1089
68.6%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1588
99.6%
Common 6
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
109
 
6.9%
62
 
3.9%
59
 
3.7%
49
 
3.1%
48
 
3.0%
46
 
2.9%
38
 
2.4%
30
 
1.9%
29
 
1.8%
29
 
1.8%
Other values (174) 1089
68.6%
Common
ValueCountFrequency (%)
- 2
33.3%
( 2
33.3%
) 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1588
99.6%
ASCII 6
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
109
 
6.9%
62
 
3.9%
59
 
3.7%
49
 
3.1%
48
 
3.0%
46
 
2.9%
38
 
2.4%
30
 
1.9%
29
 
1.8%
29
 
1.8%
Other values (174) 1089
68.6%
ASCII
ValueCountFrequency (%)
- 2
33.3%
( 2
33.3%
) 2
33.3%

성별
Categorical

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
남성
199 
여성
196 
<NA>
23 
제3의성
 
1

Length

Max length4
Median length2
Mean length2.1145585
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row남성
2nd row여성
3rd row남성
4th row여성
5th row남성

Common Values

ValueCountFrequency (%)
남성 199
47.5%
여성 196
46.8%
<NA> 23
 
5.5%
제3의성 1
 
0.2%

Length

2024-03-15T10:37:20.752329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:37:20.967505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남성 199
47.5%
여성 196
46.8%
na 23
 
5.5%
제3의성 1
 
0.2%

0세_4세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct103
Distinct (%)26.0%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean106.65152
Minimum0
Maximum4158
Zeros140
Zeros (%)33.4%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:21.196196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q318
95-th percentile370.25
Maximum4158
Range4158
Interquartile range (IQR)18

Descriptive statistics

Standard deviation477.50949
Coefficient of variation (CV)4.4772874
Kurtosis52.259524
Mean106.65152
Median Absolute Deviation (MAD)2
Skewness6.9900253
Sum42234
Variance228015.31
MonotonicityNot monotonic
2024-03-15T10:37:21.577052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 140
33.4%
1 40
 
9.5%
2 25
 
6.0%
4 14
 
3.3%
3 11
 
2.6%
7 11
 
2.6%
5 10
 
2.4%
6 7
 
1.7%
8 6
 
1.4%
9 6
 
1.4%
Other values (93) 126
30.1%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 140
33.4%
1 40
 
9.5%
2 25
 
6.0%
3 11
 
2.6%
4 14
 
3.3%
5 10
 
2.4%
6 7
 
1.7%
7 11
 
2.6%
8 6
 
1.4%
9 6
 
1.4%
ValueCountFrequency (%)
4158 1
0.2%
4117 1
0.2%
4082 1
0.2%
4002 1
0.2%
2911 1
0.2%
2568 1
0.2%
1603 1
0.2%
1523 1
0.2%
964 1
0.2%
945 1
0.2%

5세_9세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct103
Distinct (%)26.0%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean124.84848
Minimum0
Maximum5171
Zeros133
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:21.855941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q319
95-th percentile524.5
Maximum5171
Range5171
Interquartile range (IQR)19

Descriptive statistics

Standard deviation575.65018
Coefficient of variation (CV)4.6107903
Kurtosis53.064426
Mean124.84848
Median Absolute Deviation (MAD)2.5
Skewness7.1224719
Sum49440
Variance331373.13
MonotonicityNot monotonic
2024-03-15T10:37:22.398672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133
31.7%
1 42
 
10.0%
2 23
 
5.5%
3 18
 
4.3%
4 16
 
3.8%
6 10
 
2.4%
7 8
 
1.9%
10 6
 
1.4%
8 6
 
1.4%
11 6
 
1.4%
Other values (93) 128
30.5%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 133
31.7%
1 42
 
10.0%
2 23
 
5.5%
3 18
 
4.3%
4 16
 
3.8%
5 6
 
1.4%
6 10
 
2.4%
7 8
 
1.9%
8 6
 
1.4%
9 6
 
1.4%
ValueCountFrequency (%)
5171 1
0.2%
4919 1
0.2%
4631 1
0.2%
4414 1
0.2%
4030 1
0.2%
4019 1
0.2%
1269 1
0.2%
1246 1
0.2%
1032 1
0.2%
969 1
0.2%

10세_14세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct94
Distinct (%)23.7%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean87.55303
Minimum0
Maximum3324
Zeros152
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:22.891738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313
95-th percentile496.25
Maximum3324
Range3324
Interquartile range (IQR)13

Descriptive statistics

Standard deviation364.16906
Coefficient of variation (CV)4.1594112
Kurtosis46.966116
Mean87.55303
Median Absolute Deviation (MAD)2
Skewness6.5718119
Sum34671
Variance132619.1
MonotonicityNot monotonic
2024-03-15T10:37:23.389127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 152
36.3%
1 41
 
9.8%
2 21
 
5.0%
5 15
 
3.6%
7 14
 
3.3%
3 12
 
2.9%
6 9
 
2.1%
4 9
 
2.1%
8 8
 
1.9%
9 5
 
1.2%
Other values (84) 110
26.3%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 152
36.3%
1 41
 
9.8%
2 21
 
5.0%
3 12
 
2.9%
4 9
 
2.1%
5 15
 
3.6%
6 9
 
2.1%
7 14
 
3.3%
8 8
 
1.9%
9 5
 
1.2%
ValueCountFrequency (%)
3324 1
0.2%
3098 1
0.2%
2724 1
0.2%
2505 1
0.2%
2495 1
0.2%
2445 1
0.2%
1107 1
0.2%
1068 1
0.2%
758 1
0.2%
748 1
0.2%

15세_19세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct98
Distinct (%)24.7%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean132.5404
Minimum0
Maximum5356
Zeros128
Zeros (%)30.5%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:23.834168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320.25
95-th percentile555
Maximum5356
Range5356
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation548.55608
Coefficient of variation (CV)4.1387838
Kurtosis46.118086
Mean132.5404
Median Absolute Deviation (MAD)2
Skewness6.3934731
Sum52486
Variance300913.77
MonotonicityNot monotonic
2024-03-15T10:37:24.306791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
30.5%
1 52
12.4%
2 25
 
6.0%
3 21
 
5.0%
6 9
 
2.1%
4 9
 
2.1%
11 8
 
1.9%
9 7
 
1.7%
8 6
 
1.4%
5 5
 
1.2%
Other values (88) 126
30.1%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 128
30.5%
1 52
12.4%
2 25
 
6.0%
3 21
 
5.0%
4 9
 
2.1%
5 5
 
1.2%
6 9
 
2.1%
7 5
 
1.2%
8 6
 
1.4%
9 7
 
1.7%
ValueCountFrequency (%)
5356 1
0.2%
4718 1
0.2%
3799 1
0.2%
3744 1
0.2%
3351 1
0.2%
2405 1
0.2%
2297 1
0.2%
2065 1
0.2%
1704 1
0.2%
1650 1
0.2%

20세_24세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct145
Distinct (%)36.6%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean543.76263
Minimum0
Maximum28999
Zeros62
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:24.752457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q366.5
95-th percentile2289.75
Maximum28999
Range28999
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation2556.6603
Coefficient of variation (CV)4.7017947
Kurtosis77.842031
Mean543.76263
Median Absolute Deviation (MAD)10
Skewness8.3051956
Sum215330
Variance6536511.7
MonotonicityNot monotonic
2024-03-15T10:37:25.281950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
 
14.8%
1 34
 
8.1%
2 24
 
5.7%
3 22
 
5.3%
4 12
 
2.9%
5 11
 
2.6%
10 9
 
2.1%
7 8
 
1.9%
8 7
 
1.7%
12 7
 
1.7%
Other values (135) 200
47.7%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 62
14.8%
1 34
8.1%
2 24
 
5.7%
3 22
 
5.3%
4 12
 
2.9%
5 11
 
2.6%
6 7
 
1.7%
7 8
 
1.9%
8 7
 
1.7%
9 4
 
1.0%
ValueCountFrequency (%)
28999 1
0.2%
25645 1
0.2%
22580 1
0.2%
14702 1
0.2%
9976 1
0.2%
8057 1
0.2%
6447 1
0.2%
6104 1
0.2%
5904 1
0.2%
5339 1
0.2%

25세_29세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct160
Distinct (%)40.4%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean750.56566
Minimum0
Maximum23790
Zeros55
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:25.740472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15
Q391
95-th percentile3992.25
Maximum23790
Range23790
Interquartile range (IQR)89

Descriptive statistics

Standard deviation2778.7072
Coefficient of variation (CV)3.7021508
Kurtosis31.470839
Mean750.56566
Median Absolute Deviation (MAD)15
Skewness5.3305403
Sum297224
Variance7721213.9
MonotonicityNot monotonic
2024-03-15T10:37:26.259898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
13.1%
1 35
 
8.4%
2 20
 
4.8%
4 13
 
3.1%
6 11
 
2.6%
7 10
 
2.4%
5 10
 
2.4%
8 8
 
1.9%
3 7
 
1.7%
11 6
 
1.4%
Other values (150) 221
52.7%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 55
13.1%
1 35
8.4%
2 20
 
4.8%
3 7
 
1.7%
4 13
 
3.1%
5 10
 
2.4%
6 11
 
2.6%
7 10
 
2.4%
8 8
 
1.9%
9 3
 
0.7%
ValueCountFrequency (%)
23790 1
0.2%
20538 1
0.2%
20309 1
0.2%
16970 1
0.2%
15047 1
0.2%
13920 1
0.2%
12635 1
0.2%
11508 1
0.2%
10978 1
0.2%
10130 1
0.2%

30세_34세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct154
Distinct (%)38.9%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean803.14646
Minimum0
Maximum24723
Zeros55
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:26.757997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q394
95-th percentile4267.5
Maximum24723
Range24723
Interquartile range (IQR)92

Descriptive statistics

Standard deviation3132.8925
Coefficient of variation (CV)3.9007736
Kurtosis32.4835
Mean803.14646
Median Absolute Deviation (MAD)13
Skewness5.4732221
Sum318046
Variance9815015.5
MonotonicityNot monotonic
2024-03-15T10:37:27.019985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
13.1%
1 30
 
7.2%
2 25
 
6.0%
3 16
 
3.8%
4 14
 
3.3%
5 10
 
2.4%
7 10
 
2.4%
9 8
 
1.9%
11 8
 
1.9%
10 6
 
1.4%
Other values (144) 214
51.1%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 55
13.1%
1 30
7.2%
2 25
6.0%
3 16
 
3.8%
4 14
 
3.3%
5 10
 
2.4%
6 6
 
1.4%
7 10
 
2.4%
8 4
 
1.0%
9 8
 
1.9%
ValueCountFrequency (%)
24723 1
0.2%
24541 1
0.2%
22454 1
0.2%
20910 1
0.2%
20292 1
0.2%
15740 1
0.2%
14666 1
0.2%
12483 1
0.2%
11997 1
0.2%
10717 1
0.2%

35세_39세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct141
Distinct (%)35.6%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean714.88131
Minimum0
Maximum35438
Zeros66
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:27.402316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q366
95-th percentile3388
Maximum35438
Range35438
Interquartile range (IQR)65

Descriptive statistics

Standard deviation3183.5428
Coefficient of variation (CV)4.4532466
Kurtosis56.925329
Mean714.88131
Median Absolute Deviation (MAD)9
Skewness6.9882389
Sum283093
Variance10134945
MonotonicityNot monotonic
2024-03-15T10:37:27.876149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
 
15.8%
1 37
 
8.8%
2 22
 
5.3%
3 21
 
5.0%
4 15
 
3.6%
7 13
 
3.1%
15 10
 
2.4%
6 8
 
1.9%
5 7
 
1.7%
9 7
 
1.7%
Other values (131) 190
45.3%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 66
15.8%
1 37
8.8%
2 22
 
5.3%
3 21
 
5.0%
4 15
 
3.6%
5 7
 
1.7%
6 8
 
1.9%
7 13
 
3.1%
8 4
 
1.0%
9 7
 
1.7%
ValueCountFrequency (%)
35438 1
0.2%
26311 1
0.2%
21355 1
0.2%
19038 1
0.2%
18034 1
0.2%
17152 1
0.2%
13948 1
0.2%
10529 1
0.2%
10168 1
0.2%
7362 1
0.2%

40세_44세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct127
Distinct (%)32.1%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean525.06313
Minimum0
Maximum32361
Zeros78
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:28.375010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q339
95-th percentile2171.75
Maximum32361
Range32361
Interquartile range (IQR)38

Descriptive statistics

Standard deviation2516.4826
Coefficient of variation (CV)4.7927239
Kurtosis81.68168
Mean525.06313
Median Absolute Deviation (MAD)6
Skewness8.2051105
Sum207925
Variance6332684.7
MonotonicityNot monotonic
2024-03-15T10:37:28.796184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
18.6%
1 40
 
9.5%
4 21
 
5.0%
2 20
 
4.8%
3 20
 
4.8%
5 14
 
3.3%
6 11
 
2.6%
9 7
 
1.7%
10 7
 
1.7%
8 6
 
1.4%
Other values (117) 172
41.1%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 78
18.6%
1 40
9.5%
2 20
 
4.8%
3 20
 
4.8%
4 21
 
5.0%
5 14
 
3.3%
6 11
 
2.6%
7 4
 
1.0%
8 6
 
1.4%
9 7
 
1.7%
ValueCountFrequency (%)
32361 1
0.2%
19528 1
0.2%
16301 1
0.2%
14573 1
0.2%
14454 1
0.2%
11651 1
0.2%
9008 1
0.2%
7364 1
0.2%
6549 1
0.2%
5117 1
0.2%

45세_49세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct116
Distinct (%)29.3%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean399.73232
Minimum0
Maximum30587
Zeros89
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:29.061787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q327.25
95-th percentile1467
Maximum30587
Range30587
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation2147.6063
Coefficient of variation (CV)5.3726111
Kurtosis116.47122
Mean399.73232
Median Absolute Deviation (MAD)4
Skewness9.8022577
Sum158294
Variance4612212.9
MonotonicityNot monotonic
2024-03-15T10:37:29.461431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89
21.2%
1 50
 
11.9%
2 31
 
7.4%
3 19
 
4.5%
5 12
 
2.9%
4 11
 
2.6%
8 9
 
2.1%
12 8
 
1.9%
7 7
 
1.7%
9 7
 
1.7%
Other values (106) 153
36.5%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 89
21.2%
1 50
11.9%
2 31
 
7.4%
3 19
 
4.5%
4 11
 
2.6%
5 12
 
2.9%
6 7
 
1.7%
7 7
 
1.7%
8 9
 
2.1%
9 7
 
1.7%
ValueCountFrequency (%)
30587 1
0.2%
18992 1
0.2%
11546 1
0.2%
9760 1
0.2%
9612 1
0.2%
9176 1
0.2%
5974 1
0.2%
5874 1
0.2%
5294 1
0.2%
3951 1
0.2%

50세_54세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct108
Distinct (%)27.3%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean428.7702
Minimum0
Maximum44398
Zeros112
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:29.879207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q321
95-th percentile1269.75
Maximum44398
Range44398
Interquartile range (IQR)21

Descriptive statistics

Standard deviation3006.1594
Coefficient of variation (CV)7.0111201
Kurtosis161.53503
Mean428.7702
Median Absolute Deviation (MAD)3
Skewness12.165089
Sum169793
Variance9036994.2
MonotonicityNot monotonic
2024-03-15T10:37:30.143171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 112
26.7%
1 45
 
10.7%
2 23
 
5.5%
3 20
 
4.8%
4 16
 
3.8%
5 13
 
3.1%
6 11
 
2.6%
7 10
 
2.4%
9 7
 
1.7%
8 6
 
1.4%
Other values (98) 133
31.7%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 112
26.7%
1 45
10.7%
2 23
 
5.5%
3 20
 
4.8%
4 16
 
3.8%
5 13
 
3.1%
6 11
 
2.6%
7 10
 
2.4%
8 6
 
1.4%
9 7
 
1.7%
ValueCountFrequency (%)
44398 1
0.2%
35230 1
0.2%
11045 1
0.2%
9755 1
0.2%
6301 1
0.2%
5438 1
0.2%
5343 1
0.2%
5112 1
0.2%
4807 1
0.2%
4719 1
0.2%

55세_59세
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct99
Distinct (%)25.0%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean383.63384
Minimum0
Maximum45909
Zeros113
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:30.548115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q315.25
95-th percentile917
Maximum45909
Range45909
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation3203.1364
Coefficient of variation (CV)8.3494626
Kurtosis177.54148
Mean383.63384
Median Absolute Deviation (MAD)2
Skewness13.082672
Sum151919
Variance10260083
MonotonicityNot monotonic
2024-03-15T10:37:30.989303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 113
27.0%
1 62
14.8%
2 38
 
9.1%
3 18
 
4.3%
4 17
 
4.1%
6 7
 
1.7%
12 6
 
1.4%
5 6
 
1.4%
11 6
 
1.4%
10 6
 
1.4%
Other values (89) 117
27.9%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 113
27.0%
1 62
14.8%
2 38
 
9.1%
3 18
 
4.3%
4 17
 
4.1%
5 6
 
1.4%
6 7
 
1.7%
7 4
 
1.0%
8 5
 
1.2%
9 3
 
0.7%
ValueCountFrequency (%)
45909 1
0.2%
42326 1
0.2%
7340 1
0.2%
6156 1
0.2%
5277 1
0.2%
4614 1
0.2%
4143 1
0.2%
3837 1
0.2%
3050 1
0.2%
2288 1
0.2%

60세이상
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct106
Distinct (%)26.8%
Missing23
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean670.34596
Minimum0
Maximum95128
Zeros96
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-15T10:37:31.371077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q321.25
95-th percentile1100.5
Maximum95128
Range95128
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation6371.5241
Coefficient of variation (CV)9.5048296
Kurtosis188.78366
Mean670.34596
Median Absolute Deviation (MAD)3
Skewness13.578406
Sum265457
Variance40596320
MonotonicityNot monotonic
2024-03-15T10:37:31.771115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96
22.9%
1 49
 
11.7%
2 36
 
8.6%
3 28
 
6.7%
4 17
 
4.1%
5 12
 
2.9%
7 10
 
2.4%
6 7
 
1.7%
8 6
 
1.4%
9 6
 
1.4%
Other values (96) 129
30.8%
(Missing) 23
 
5.5%
ValueCountFrequency (%)
0 96
22.9%
1 49
11.7%
2 36
 
8.6%
3 28
 
6.7%
4 17
 
4.1%
5 12
 
2.9%
6 7
 
1.7%
7 10
 
2.4%
8 6
 
1.4%
9 6
 
1.4%
ValueCountFrequency (%)
95128 1
0.2%
81806 1
0.2%
13206 1
0.2%
11103 1
0.2%
5995 1
0.2%
5419 1
0.2%
4540 1
0.2%
3697 1
0.2%
3271 1
0.2%
3001 1
0.2%

Interactions

2024-03-15T10:37:13.057023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:34.215471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:37.808465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:41.094629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:44.074237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:46.508595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:49.911659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:53.636383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:56.836916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:59.919544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:03.449229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:06.299713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:09.290467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:13.206019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:34.449651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:38.308264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:41.326502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:44.321058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:46.672761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:50.179728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:53.949495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:57.091738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:00.171800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:03.695990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:06.442008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:09.536956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:13.428194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:34.718865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:38.550618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:41.618999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:44.571849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:46.829295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:50.456179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:54.242646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:57.314371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:00.422401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:03.948052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:06.621464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:09.789568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:13.578818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:35.032977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:38.805262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:41.891747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:44.780757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:47.007992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:50.724677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:54.548188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:57.540745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:00.679759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:04.206040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:06.794416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:10.053382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:13.827265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:35.294333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:39.054682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:42.048098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:44.939372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:47.163410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:50.997276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:54.861590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:57.831936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:00.933273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:04.460148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:06.939703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:10.309564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:14.094072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:35.573813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:39.315441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:42.252839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:45.100369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:47.333876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:51.258888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:55.245022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:58.017292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:01.195370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:04.658409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:07.193835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:10.609675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:14.332600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:35.832933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:39.558739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:42.502331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:45.310198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:47.584229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:51.540151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:55.493631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:58.165103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:01.439845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:04.857388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:07.375383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:10.874611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:14.583232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:36.083885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:39.808134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:42.711223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:45.485326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:47.912351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:51.871422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:55.639407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:58.365936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:01.689034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:05.011443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:07.615780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:11.268772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:14.742890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:36.376367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:40.024722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:42.931380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:45.665519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:48.272129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:52.154099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:55.823354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:58.641736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:01.993859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:05.178254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:07.880376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:11.584929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:14.981925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:36.658327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:40.233483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:43.190684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:45.823484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:48.577864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:52.410975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:55.998164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:58.909737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:02.256765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:05.404845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:08.210753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:11.935435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:15.173262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:36.944383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:40.490196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:43.449878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:46.002774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:48.866616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:52.708245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:56.160307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:59.168374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:02.523283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:05.663849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:08.454166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:12.290250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:15.316288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:37.222473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:40.723732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:43.694237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:46.146677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:49.198199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:53.083177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:56.367523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:59.417480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:02.742050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:05.913761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:08.712048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:12.543659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:15.455681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:37.547838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:40.949516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:43.844192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:46.297847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:49.650902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:53.372952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:56.595345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:36:59.664425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:03.190548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:06.153070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:09.015288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:12.813669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:37:32.123902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대륙성별0세_4세5세_9세10세_14세15세_19세20세_24세25세_29세30세_34세35세_39세40세_44세45세_49세50세_54세55세_59세60세이상
대륙1.0000.3980.6290.4580.5480.3310.3710.3130.3290.2390.2160.2790.2690.2320.483
성별0.3981.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
0세_4세0.6290.0001.0000.9230.9850.8640.8330.7670.7860.7690.7610.7270.7440.7800.802
5세_9세0.4580.0000.9231.0000.9480.9180.8330.8080.8700.8920.8320.9460.8550.9940.923
10세_14세0.5480.0000.9850.9481.0000.8660.8450.8380.8210.8220.7920.7660.8150.8280.773
15세_19세0.3310.0000.8640.9180.8661.0000.9360.9190.9520.9590.8460.8490.8470.9820.764
20세_24세0.3710.0000.8330.8330.8450.9361.0000.9090.8960.9110.9650.8000.8190.7980.851
25세_29세0.3130.0000.7670.8080.8380.9190.9091.0000.8960.9040.8720.8800.9420.8040.680
30세_34세0.3290.0000.7860.8700.8210.9520.8960.8961.0000.9640.8830.9000.8890.9670.764
35세_39세0.2390.0000.7690.8920.8220.9590.9110.9040.9641.0000.9690.9900.9780.9920.917
40세_44세0.2160.0000.7610.8320.7920.8460.9650.8720.8830.9691.0000.9620.9560.8890.990
45세_49세0.2790.0000.7270.9460.7660.8490.8000.8800.9000.9900.9621.0000.9440.9890.973
50세_54세0.2690.0000.7440.8550.8150.8470.8190.9420.8890.9780.9560.9441.0000.8680.885
55세_59세0.2320.0000.7800.9940.8280.9820.7980.8040.9670.9920.8890.9890.8681.0000.708
60세이상0.4830.0000.8020.9230.7730.7640.8510.6800.7640.9170.9900.9730.8850.7081.000
2024-03-15T10:37:32.394153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별대륙
성별1.0000.290
대륙0.2901.000
2024-03-15T10:37:32.576287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
0세_4세5세_9세10세_14세15세_19세20세_24세25세_29세30세_34세35세_39세40세_44세45세_49세50세_54세55세_59세60세이상대륙성별
0세_4세1.0000.9090.8620.8460.8550.8720.8800.8800.8760.8550.8350.8140.7950.2630.000
5세_9세0.9091.0000.9040.8820.8630.8790.8730.8880.8800.8750.8670.8400.8320.2940.000
10세_14세0.8620.9041.0000.8810.8490.8440.8440.8530.8820.8680.8670.8570.8560.2170.000
15세_19세0.8460.8820.8811.0000.9170.8860.8680.8540.8530.8560.8620.8330.8370.1810.000
20세_24세0.8550.8630.8490.9171.0000.9470.9210.8980.8850.8770.8740.8400.8240.2090.000
25세_29세0.8720.8790.8440.8860.9471.0000.9630.9420.9280.9040.8850.8390.8350.1630.000
30세_34세0.8800.8730.8440.8680.9210.9631.0000.9600.9480.9090.8830.8430.8320.1790.000
35세_39세0.8800.8880.8530.8540.8980.9420.9601.0000.9480.9180.8870.8520.8440.1280.000
40세_44세0.8760.8800.8820.8530.8850.9280.9480.9481.0000.9330.9050.8890.8710.1170.000
45세_49세0.8550.8750.8680.8560.8770.9040.9090.9180.9331.0000.9260.8920.8620.1700.000
50세_54세0.8350.8670.8670.8620.8740.8850.8830.8870.9050.9261.0000.9130.8820.1750.000
55세_59세0.8140.8400.8570.8330.8400.8390.8430.8520.8890.8920.9131.0000.8930.1590.000
60세이상0.7950.8320.8560.8370.8240.8350.8320.8440.8710.8620.8820.8931.0000.3510.000
대륙0.2630.2940.2170.1810.2090.1630.1790.1280.1170.1700.1750.1590.3511.0000.290
성별0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2901.000

Missing values

2024-03-15T10:37:15.752824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:37:16.383465image/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.
2024-03-15T10:37:16.859692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

대륙국적성별0세_4세5세_9세10세_14세15세_19세20세_24세25세_29세30세_34세35세_39세40세_44세45세_49세50세_54세55세_59세60세이상
0아시아주한국계중국인남성41174030250516503729929224723354383236130587443984590981806
1아시아주한국계중국인여성40824019244517043384830320910263111952818992352304232695128
2아시아주중국남성415851713324374414702100641248313948116519760975561565419
3아시아주중국여성40024919309847182258015047146661715214454115461104573405995
4아시아주베트남남성94557543437992564523790224541903890085874480738373001
5아시아주베트남여성91154154553562899920538157401052965495974630152774540
6아시아주타이남성209264232708422716970202921803414573917654382214946
7아시아주타이여성1853163579085904203092454121355163019612511222881210
8아시아주우즈베키스탄남성1603126911072065805790777780736251173160183011941979
9아시아주우즈베키스탄여성1523124610681055226728283325332827452107155412193271
대륙국적성별0세_4세5세_9세10세_14세15세_19세20세_24세25세_29세30세_34세35세_39세40세_44세45세_49세50세_54세55세_59세60세이상
409<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
410<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
411<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
412<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
413<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
414<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
415<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
416<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
417<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
418<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

대륙국적성별0세_4세5세_9세10세_14세15세_19세20세_24세25세_29세30세_34세35세_39세40세_44세45세_49세50세_54세55세_59세60세이상# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>23