Overview

Dataset statistics

Number of variables15
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory138.7 B

Variable types

Text1
Numeric14

Dataset

Description경기도 화성시 인구현황에 관한 데이터로 행정구역, 인구수, 남자인구수, 여자인구수, 연령별인구수에 대한 데이터를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15097888/fileData.do

Alerts

인구수 is highly overall correlated with 남자인구수 and 9 other fieldsHigh correlation
남자인구수 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
여자인구수 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
0-9세 is highly overall correlated with 인구수 and 8 other fieldsHigh correlation
10-19세 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
20-29세 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
30-39세 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
40-49세 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
50-59세 is highly overall correlated with 인구수 and 9 other fieldsHigh correlation
60-69세 is highly overall correlated with 인구수 and 11 other fieldsHigh correlation
70-79세 is highly overall correlated with 인구수 and 10 other fieldsHigh correlation
80-89세 is highly overall correlated with 60-69세 and 3 other fieldsHigh correlation
90-99세 is highly overall correlated with 60-69세 and 3 other fieldsHigh correlation
100세 이상 is highly overall correlated with 80-89세 and 1 other fieldsHigh correlation
행정구역 has unique valuesUnique
인구수 has unique valuesUnique
남자인구수 has unique valuesUnique
여자인구수 has unique valuesUnique
0-9세 has unique valuesUnique
10-19세 has unique valuesUnique
20-29세 has unique valuesUnique
30-39세 has unique valuesUnique
40-49세 has unique valuesUnique
50-59세 has unique valuesUnique
60-69세 has unique valuesUnique
70-79세 has unique valuesUnique
100세 이상 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2023-12-12 06:34:10.622253
Analysis finished2023-12-12 06:34:32.810674
Duration22.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T15:34:32.962788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3571429
Min length3

Characters and Unicode

Total characters94
Distinct characters41
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row봉담읍
2nd row우정읍
3rd row향남읍
4th row남양읍
5th row매송면
ValueCountFrequency (%)
봉담읍 1
 
3.6%
우정읍 1
 
3.6%
동탄7동 1
 
3.6%
동탄6동 1
 
3.6%
동탄5동 1
 
3.6%
동탄4동 1
 
3.6%
동탄3동 1
 
3.6%
동탄2동 1
 
3.6%
동탄1동 1
 
3.6%
화산동 1
 
3.6%
Other values (18) 18
64.3%
2023-12-12T15:34:33.398513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
24.5%
9
 
9.6%
9
 
9.6%
4
 
4.3%
3
 
3.2%
2
 
2.1%
2
 
2.1%
2 2
 
2.1%
1 2
 
2.1%
2
 
2.1%
Other values (31) 36
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
89.4%
Decimal Number 10
 
10.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
27.4%
9
 
10.7%
9
 
10.7%
4
 
4.8%
3
 
3.6%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (23) 26
31.0%
Decimal Number
ValueCountFrequency (%)
2 2
20.0%
1 2
20.0%
7 1
10.0%
6 1
10.0%
5 1
10.0%
4 1
10.0%
3 1
10.0%
8 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
89.4%
Common 10
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
27.4%
9
 
10.7%
9
 
10.7%
4
 
4.8%
3
 
3.6%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (23) 26
31.0%
Common
ValueCountFrequency (%)
2 2
20.0%
1 2
20.0%
7 1
10.0%
6 1
10.0%
5 1
10.0%
4 1
10.0%
3 1
10.0%
8 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
89.4%
ASCII 10
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
23
27.4%
9
 
10.7%
9
 
10.7%
4
 
4.8%
3
 
3.6%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (23) 26
31.0%
ASCII
ValueCountFrequency (%)
2 2
20.0%
1 2
20.0%
7 1
10.0%
6 1
10.0%
5 1
10.0%
4 1
10.0%
3 1
10.0%
8 1
10.0%

인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32816.607
Minimum4009
Maximum92796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:33.563104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4009
5-th percentile6863.3
Q110759.75
median29335
Q346654.25
95-th percentile87517.1
Maximum92796
Range88787
Interquartile range (IQR)35894.5

Descriptive statistics

Standard deviation25371.605
Coefficient of variation (CV)0.77313309
Kurtosis0.50203371
Mean32816.607
Median Absolute Deviation (MAD)18716
Skewness1.0246633
Sum918865
Variance6.4371833 × 108
MonotonicityNot monotonic
2023-12-12T15:34:33.716261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
88068 1
 
3.6%
36815 1
 
3.6%
33251 1
 
3.6%
92796 1
 
3.6%
42024 1
 
3.6%
46077 1
 
3.6%
52977 1
 
3.6%
41016 1
 
3.6%
34560 1
 
3.6%
50518 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
4009 1
3.6%
6815 1
3.6%
6953 1
3.6%
7000 1
3.6%
7208 1
3.6%
10027 1
3.6%
10177 1
3.6%
10954 1
3.6%
11266 1
3.6%
16257 1
3.6%
ValueCountFrequency (%)
92796 1
3.6%
88068 1
3.6%
86494 1
3.6%
52977 1
3.6%
51015 1
3.6%
50518 1
3.6%
48386 1
3.6%
46077 1
3.6%
42024 1
3.6%
41016 1
3.6%

남자인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17051.321
Minimum2482
Maximum46582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:33.870642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2482
5-th percentile3755.1
Q16081
median14743.5
Q323846.75
95-th percentile45788.3
Maximum46582
Range44100
Interquartile range (IQR)17765.75

Descriptive statistics

Standard deviation12937.612
Coefficient of variation (CV)0.75874544
Kurtosis0.51412496
Mean17051.321
Median Absolute Deviation (MAD)8691.5
Skewness1.0542771
Sum477437
Variance1.6738182 × 108
MonotonicityNot monotonic
2023-12-12T15:34:34.052057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
44604 1
 
3.6%
18922 1
 
3.6%
16740 1
 
3.6%
46582 1
 
3.6%
21639 1
 
3.6%
23082 1
 
3.6%
26305 1
 
3.6%
21174 1
 
3.6%
16988 1
 
3.6%
26141 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
2482 1
3.6%
3634 1
3.6%
3980 1
3.6%
4003 1
3.6%
4180 1
3.6%
5914 1
3.6%
5994 1
3.6%
6110 1
3.6%
6567 1
3.6%
8261 1
3.6%
ValueCountFrequency (%)
46582 1
3.6%
46426 1
3.6%
44604 1
3.6%
27722 1
3.6%
26361 1
3.6%
26305 1
3.6%
26141 1
3.6%
23082 1
3.6%
21639 1
3.6%
21174 1
3.6%

여자인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15765.286
Minimum1527
Maximum46214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:34.209130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1527
5-th percentile2859.45
Q14590
median14591.5
Q322267.5
95-th percentile42275.4
Maximum46214
Range44687
Interquartile range (IQR)17677.5

Descriptive statistics

Standard deviation12476.631
Coefficient of variation (CV)0.79139898
Kurtosis0.53985638
Mean15765.286
Median Absolute Deviation (MAD)9166.5
Skewness1.0051011
Sum441428
Variance1.5566632 × 108
MonotonicityNot monotonic
2023-12-12T15:34:34.360600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
43464 1
 
3.6%
17893 1
 
3.6%
16511 1
 
3.6%
46214 1
 
3.6%
20385 1
 
3.6%
22995 1
 
3.6%
26672 1
 
3.6%
19842 1
 
3.6%
17572 1
 
3.6%
24377 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1527 1
3.6%
2773 1
3.6%
3020 1
3.6%
3181 1
3.6%
3205 1
3.6%
3917 1
3.6%
4263 1
3.6%
4699 1
3.6%
4960 1
3.6%
7903 1
3.6%
ValueCountFrequency (%)
46214 1
3.6%
43464 1
3.6%
40068 1
3.6%
26672 1
3.6%
24377 1
3.6%
23293 1
3.6%
22995 1
3.6%
22025 1
3.6%
20385 1
3.6%
19842 1
3.6%

0-9세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3466.2143
Minimum83
Maximum14284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:34.516724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile158.75
Q1335.75
median3334
Q35241.5
95-th percentile8496.25
Maximum14284
Range14201
Interquartile range (IQR)4905.75

Descriptive statistics

Standard deviation3421.7196
Coefficient of variation (CV)0.98716331
Kurtosis2.139034
Mean3466.2143
Median Absolute Deviation (MAD)2564.5
Skewness1.2559612
Sum97054
Variance11708165
MonotonicityNot monotonic
2023-12-12T15:34:34.983920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7765 1
 
3.6%
3506 1
 
3.6%
5835 1
 
3.6%
14284 1
 
3.6%
5414 1
 
3.6%
5710 1
 
3.6%
7381 1
 
3.6%
4547 1
 
3.6%
3162 1
 
3.6%
5184 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
83 1
3.6%
143 1
3.6%
188 1
3.6%
199 1
3.6%
239 1
3.6%
281 1
3.6%
323 1
3.6%
340 1
3.6%
367 1
3.6%
706 1
3.6%
ValueCountFrequency (%)
14284 1
3.6%
8890 1
3.6%
7765 1
3.6%
7381 1
3.6%
5835 1
3.6%
5710 1
3.6%
5414 1
3.6%
5184 1
3.6%
4813 1
3.6%
4575 1
3.6%

10-19세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3827.7857
Minimum142
Maximum11578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:35.156119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum142
5-th percentile305.55
Q1629.75
median3550.5
Q35014.25
95-th percentile10623.05
Maximum11578
Range11436
Interquartile range (IQR)4384.5

Descriptive statistics

Standard deviation3479.7538
Coefficient of variation (CV)0.90907747
Kurtosis-0.033975567
Mean3827.7857
Median Absolute Deviation (MAD)2858
Skewness0.92605288
Sum107178
Variance12108686
MonotonicityNot monotonic
2023-12-12T15:34:35.315989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
10417 1
 
3.6%
3682 1
 
3.6%
3419 1
 
3.6%
11578 1
 
3.6%
4756 1
 
3.6%
4859 1
 
3.6%
9921 1
 
3.6%
5480 1
 
3.6%
6591 1
 
3.6%
6731 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
142 1
3.6%
287 1
3.6%
340 1
3.6%
350 1
3.6%
427 1
3.6%
435 1
3.6%
539 1
3.6%
660 1
3.6%
725 1
3.6%
1385 1
3.6%
ValueCountFrequency (%)
11578 1
3.6%
10734 1
3.6%
10417 1
3.6%
9921 1
3.6%
6731 1
3.6%
6591 1
3.6%
5480 1
3.6%
4859 1
3.6%
4756 1
3.6%
4445 1
3.6%

20-29세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3923.4286
Minimum378
Maximum11095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:35.476539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum378
5-th percentile563.55
Q1947.5
median3152.5
Q36209.5
95-th percentile9539.45
Maximum11095
Range10717
Interquartile range (IQR)5262

Descriptive statistics

Standard deviation3140.9208
Coefficient of variation (CV)0.80055512
Kurtosis-0.50196525
Mean3923.4286
Median Absolute Deviation (MAD)2303.5
Skewness0.72412415
Sum109856
Variance9865383.7
MonotonicityNot monotonic
2023-12-12T15:34:35.666688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
11095 1
 
3.6%
6262 1
 
3.6%
2776 1
 
3.6%
8712 1
 
3.6%
6073 1
 
3.6%
5251 1
 
3.6%
4312 1
 
3.6%
6192 1
 
3.6%
3809 1
 
3.6%
8024 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
378 1
3.6%
510 1
3.6%
663 1
3.6%
710 1
3.6%
777 1
3.6%
921 1
3.6%
931 1
3.6%
953 1
3.6%
1244 1
3.6%
1766 1
3.6%
ValueCountFrequency (%)
11095 1
3.6%
9985 1
3.6%
8712 1
3.6%
8024 1
3.6%
7347 1
3.6%
6659 1
3.6%
6262 1
3.6%
6192 1
3.6%
6073 1
3.6%
5251 1
3.6%

30-39세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5386.9286
Minimum391
Maximum19186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:35.830014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum391
5-th percentile557.9
Q1951
median3981.5
Q38739.5
95-th percentile13268.4
Maximum19186
Range18795
Interquartile range (IQR)7788.5

Descriptive statistics

Standard deviation4882.744
Coefficient of variation (CV)0.90640593
Kurtosis0.68586347
Mean5386.9286
Median Absolute Deviation (MAD)3247
Skewness0.99346939
Sum150834
Variance23841189
MonotonicityNot monotonic
2023-12-12T15:34:35.987402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
12270 1
 
3.6%
6301 1
 
3.6%
7889 1
 
3.6%
19186 1
 
3.6%
8685 1
 
3.6%
10536 1
 
3.6%
7247 1
 
3.6%
7347 1
 
3.6%
3329 1
 
3.6%
8943 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
391 1
3.6%
539 1
3.6%
593 1
3.6%
753 1
3.6%
780 1
3.6%
868 1
3.6%
900 1
3.6%
968 1
3.6%
1141 1
3.6%
1561 1
3.6%
ValueCountFrequency (%)
19186 1
3.6%
13806 1
3.6%
12270 1
3.6%
10536 1
3.6%
9908 1
3.6%
8943 1
3.6%
8903 1
3.6%
8685 1
3.6%
7889 1
3.6%
7347 1
3.6%

40-49세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6444.6429
Minimum476
Maximum20727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:36.135039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum476
5-th percentile751.15
Q11361.25
median5957.5
Q38634.25
95-th percentile17556.7
Maximum20727
Range20251
Interquartile range (IQR)7273

Descriptive statistics

Standard deviation5583.6869
Coefficient of variation (CV)0.86640751
Kurtosis0.54871448
Mean6444.6429
Median Absolute Deviation (MAD)3649
Skewness1.0241032
Sum180450
Variance31177560
MonotonicityNot monotonic
2023-12-12T15:34:36.286408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
16518 1
 
3.6%
6473 1
 
3.6%
7174 1
 
3.6%
20727 1
 
3.6%
8570 1
 
3.6%
9266 1
 
3.6%
13883 1
 
3.6%
8474 1
 
3.6%
8254 1
 
3.6%
9856 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
476 1
3.6%
727 1
3.6%
796 1
3.6%
815 1
3.6%
939 1
3.6%
1271 1
3.6%
1302 1
3.6%
1381 1
3.6%
1438 1
3.6%
2558 1
3.6%
ValueCountFrequency (%)
20727 1
3.6%
18116 1
3.6%
16518 1
3.6%
13883 1
3.6%
9856 1
3.6%
9266 1
3.6%
8827 1
3.6%
8570 1
3.6%
8474 1
3.6%
8430 1
3.6%

50-59세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4683.6786
Minimum838
Maximum13857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:36.433912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum838
5-th percentile1281.45
Q12203
median4028.5
Q35719.75
95-th percentile11559.55
Maximum13857
Range13019
Interquartile range (IQR)3516.75

Descriptive statistics

Standard deviation3284.0433
Coefficient of variation (CV)0.70116753
Kurtosis1.5830688
Mean4683.6786
Median Absolute Deviation (MAD)1856.5
Skewness1.3172364
Sum131143
Variance10784941
MonotonicityNot monotonic
2023-12-12T15:34:36.566745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
13857 1
 
3.6%
5184 1
 
3.6%
3199 1
 
3.6%
10125 1
 
3.6%
5036 1
 
3.6%
5166 1
 
3.6%
6681 1
 
3.6%
4794 1
 
3.6%
5427 1
 
3.6%
7330 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
838 1
3.6%
1244 1
3.6%
1351 1
3.6%
1441 1
3.6%
1492 1
3.6%
2032 1
3.6%
2110 1
3.6%
2234 1
3.6%
2374 1
3.6%
2862 1
3.6%
ValueCountFrequency (%)
13857 1
3.6%
12332 1
3.6%
10125 1
3.6%
7881 1
3.6%
7330 1
3.6%
6681 1
3.6%
6598 1
3.6%
5427 1
3.6%
5184 1
3.6%
5166 1
3.6%

60-69세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3074.3571
Minimum973
Maximum9701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:36.705733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum973
5-th percentile1466.5
Q11938.25
median2370.5
Q33246.75
95-th percentile7139.5
Maximum9701
Range8728
Interquartile range (IQR)1308.5

Descriptive statistics

Standard deviation1983.392
Coefficient of variation (CV)0.64514042
Kurtosis4.2380019
Mean3074.3571
Median Absolute Deviation (MAD)626
Skewness2.0532072
Sum86082
Variance3933844
MonotonicityNot monotonic
2023-12-12T15:34:36.875165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9701 1
 
3.6%
3234 1
 
3.6%
2042 1
 
3.6%
5621 1
 
3.6%
2304 1
 
3.6%
3285 1
 
3.6%
2394 1
 
3.6%
2373 1
 
3.6%
2368 1
 
3.6%
2869 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
973 1
3.6%
1456 1
3.6%
1486 1
3.6%
1526 1
3.6%
1769 1
3.6%
1823 1
3.6%
1924 1
3.6%
1943 1
3.6%
2042 1
3.6%
2253 1
3.6%
ValueCountFrequency (%)
9701 1
3.6%
7549 1
3.6%
6379 1
3.6%
5621 1
3.6%
4152 1
3.6%
3507 1
3.6%
3285 1
3.6%
3234 1
3.6%
3210 1
3.6%
3021 1
3.6%

70-79세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1298.7143
Minimum362
Maximum4263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:36.993484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum362
5-th percentile499
Q1813.75
median1087.5
Q31485.5
95-th percentile3001.9
Maximum4263
Range3901
Interquartile range (IQR)671.75

Descriptive statistics

Standard deviation844.9145
Coefficient of variation (CV)0.65057766
Kurtosis5.3921617
Mean1298.7143
Median Absolute Deviation (MAD)320
Skewness2.1676886
Sum36364
Variance713880.51
MonotonicityNot monotonic
2023-12-12T15:34:37.119209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4263 1
 
3.6%
1477 1
 
3.6%
642 1
 
3.6%
1840 1
 
3.6%
795 1
 
3.6%
1375 1
 
3.6%
820 1
 
3.6%
1237 1
 
3.6%
1104 1
 
3.6%
1083 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
362 1
3.6%
422 1
3.6%
642 1
3.6%
683 1
3.6%
733 1
3.6%
740 1
3.6%
795 1
3.6%
820 1
3.6%
855 1
3.6%
894 1
3.6%
ValueCountFrequency (%)
4263 1
3.6%
3256 1
3.6%
2530 1
3.6%
1840 1
3.6%
1727 1
3.6%
1646 1
3.6%
1511 1
3.6%
1477 1
3.6%
1375 1
3.6%
1278 1
3.6%

80-89세
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617.67857
Minimum168
Maximum1913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:37.247305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile244.9
Q1403
median527.5
Q3675.25
95-th percentile1445.55
Maximum1913
Range1745
Interquartile range (IQR)272.25

Descriptive statistics

Standard deviation389.53401
Coefficient of variation (CV)0.63064194
Kurtosis4.5601732
Mean617.67857
Median Absolute Deviation (MAD)153
Skewness2.0170023
Sum17295
Variance151736.74
MonotonicityNot monotonic
2023-12-12T15:34:37.387003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
445 2
 
7.1%
1913 1
 
3.6%
616 1
 
3.6%
233 1
 
3.6%
634 1
 
3.6%
334 1
 
3.6%
550 1
 
3.6%
293 1
 
3.6%
497 1
 
3.6%
473 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
168 1
3.6%
233 1
3.6%
267 1
3.6%
293 1
3.6%
310 1
3.6%
334 1
3.6%
361 1
3.6%
417 1
3.6%
445 2
7.1%
459 1
3.6%
ValueCountFrequency (%)
1913 1
3.6%
1595 1
3.6%
1168 1
3.6%
927 1
3.6%
800 1
3.6%
752 1
3.6%
700 1
3.6%
667 1
3.6%
644 1
3.6%
634 1
3.6%

90-99세
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.357143
Minimum26
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:37.509469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile39.05
Q150
median75.5
Q3102
95-th percentile215.4
Maximum255
Range229
Interquartile range (IQR)52

Descriptive statistics

Standard deviation57.711424
Coefficient of variation (CV)0.63870351
Kurtosis2.0293261
Mean90.357143
Median Absolute Deviation (MAD)25.5
Skewness1.5611659
Sum2530
Variance3330.6085
MonotonicityNot monotonic
2023-12-12T15:34:37.647924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
41 2
 
7.1%
74 2
 
7.1%
50 2
 
7.1%
255 1
 
3.6%
146 1
 
3.6%
87 1
 
3.6%
56 1
 
3.6%
42 1
 
3.6%
92 1
 
3.6%
45 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
26 1
3.6%
38 1
3.6%
41 2
7.1%
42 1
3.6%
45 1
3.6%
50 2
7.1%
55 1
3.6%
56 1
3.6%
57 1
3.6%
61 1
3.6%
ValueCountFrequency (%)
255 1
3.6%
228 1
3.6%
192 1
3.6%
162 1
3.6%
146 1
3.6%
108 1
3.6%
105 1
3.6%
101 1
3.6%
97 1
3.6%
92 1
3.6%

100세 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8214286
Minimum0
Maximum14
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T15:34:37.748791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33.25
95-th percentile7.6
Maximum14
Range14
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.8551911
Coefficient of variation (CV)1.0119665
Kurtosis8.6723384
Mean2.8214286
Median Absolute Deviation (MAD)1
Skewness2.7025107
Sum79
Variance8.1521164
MonotonicityNot monotonic
2023-12-12T15:34:37.844537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 9
32.1%
2 8
28.6%
4 3
 
10.7%
3 3
 
10.7%
5 2
 
7.1%
14 1
 
3.6%
9 1
 
3.6%
0 1
 
3.6%
ValueCountFrequency (%)
0 1
 
3.6%
1 9
32.1%
2 8
28.6%
3 3
 
10.7%
4 3
 
10.7%
5 2
 
7.1%
9 1
 
3.6%
14 1
 
3.6%
ValueCountFrequency (%)
14 1
 
3.6%
9 1
 
3.6%
5 2
 
7.1%
4 3
 
10.7%
3 3
 
10.7%
2 8
28.6%
1 9
32.1%
0 1
 
3.6%

Interactions

2023-12-12T15:34:30.770503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.140526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.465835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.765191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.621521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.110479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.688201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.051818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:21.851713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.503857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.885723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.149483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.844817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.479447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.867898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.228763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.536608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.857377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.715189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.220261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.781066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.146844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:21.950352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.586952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.986881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.232172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.951006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.563690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.967861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.334435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.604367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.940687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.809618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.303307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.867769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.247036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.070901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.669357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.063891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.317056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.068625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.636940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.077669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.445639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.684136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:14.019523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.908885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.400538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.967950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.338595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.183885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.770521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.137032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.403620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.185504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.718858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.231365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.595155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.767916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:14.110601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.017641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.509674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.062344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.438732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.309835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.867297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.212440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.505426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.298407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.809892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.336819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.692290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.857293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:14.508821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.117758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.620273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.161971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.525098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.447900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.952128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.341191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.593108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.427596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.890908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.442957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.771891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.935375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:14.609084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.223760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.727814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.248188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.606387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.553492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.036358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.430703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.693473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.543084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.978379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.544287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.867622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.042836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:14.763164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.366802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.863129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.351548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.711776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.667217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.126695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.516886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.810032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.666261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.082494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.666775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:11.963867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.152443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:14.884194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.494095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:17.986698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.455611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.803618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.777910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.219444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.603950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.915471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.795475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.164805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.774117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.045366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.269876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.059332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.612103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.097203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.547029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:20.892275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.899330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.303610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.690566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.013421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:28.925440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.281594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:31.879428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.119430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.394739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.157748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.713477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.186346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.642425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:21.305883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.011852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.417405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.773154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.106652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.040451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.355986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:32.005649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.209587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.474993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.277964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.810789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.305285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.741954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:21.414414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.123863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.558447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.868759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.201946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.145974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.458178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:32.120974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.289127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.588005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.394797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.912087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.452844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.854224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:21.586843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.258953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.682992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:25.975111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.301789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.249762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.564312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:32.224949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:12.360742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:13.678158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:15.495826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:16.999103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:18.565200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:19.950502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:21.714864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.377443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:24.770121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:26.063468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:27.404639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:29.345666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:30.656370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:34:37.926700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역인구수남자인구수여자인구수0-9세10-19세20-29세30-39세40-49세50-59세60-69세70-79세80-89세90-99세100세 이상
행정구역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구수1.0001.0000.9990.9760.7920.8450.8430.7970.8490.7750.4780.4920.3950.3370.493
남자인구수1.0000.9991.0000.9670.7830.8700.8510.7510.8440.8160.4410.4180.3310.2800.461
여자인구수1.0000.9760.9671.0000.9550.9360.8950.8520.9150.8520.8330.6280.7830.7620.549
0-9세1.0000.7920.7830.9551.0000.9440.8300.8990.9040.8360.8770.6450.6530.7280.060
10-19세1.0000.8450.8700.9360.9441.0000.8230.7210.8720.8100.7270.4890.6160.6540.257
20-29세1.0000.8430.8510.8950.8300.8231.0000.8970.8940.8630.8640.7920.7510.7450.732
30-39세1.0000.7970.7510.8520.8990.7210.8971.0000.9620.9460.8610.7640.6780.7290.777
40-49세1.0000.8490.8440.9150.9040.8720.8940.9621.0000.9590.8480.7170.7200.6740.707
50-59세1.0000.7750.8160.8520.8360.8100.8630.9460.9591.0000.9120.8730.8270.8270.850
60-69세1.0000.4780.4410.8330.8770.7270.8640.8610.8480.9121.0000.9060.9400.9300.788
70-79세1.0000.4920.4180.6280.6450.4890.7920.7640.7170.8730.9061.0000.8780.8810.752
80-89세1.0000.3950.3310.7830.6530.6160.7510.6780.7200.8270.9400.8781.0000.9760.809
90-99세1.0000.3370.2800.7620.7280.6540.7450.7290.6740.8270.9300.8810.9761.0000.829
100세 이상1.0000.4930.4610.5490.0600.2570.7320.7770.7070.8500.7880.7520.8090.8291.000
2023-12-12T15:34:38.065359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구수남자인구수여자인구수0-9세10-19세20-29세30-39세40-49세50-59세60-69세70-79세80-89세90-99세100세 이상
인구수1.0000.9930.9960.9540.9530.9670.9640.9790.9760.8260.5950.2640.1420.325
남자인구수0.9931.0000.9850.9470.9390.9690.9740.9770.9740.8330.6020.2780.1600.310
여자인구수0.9960.9851.0000.9580.9640.9640.9620.9820.9700.8070.5780.2380.1080.314
0-9세0.9540.9470.9581.0000.9310.9050.9670.9710.8890.7220.4430.1150.0170.221
10-19세0.9530.9390.9640.9311.0000.9290.9150.9730.9410.7350.5170.1590.0020.191
20-29세0.9670.9690.9640.9050.9291.0000.9550.9470.9680.8350.6550.3290.1870.267
30-39세0.9640.9740.9620.9670.9150.9551.0000.9710.9230.7890.5530.2290.1250.261
40-49세0.9790.9770.9820.9710.9730.9470.9711.0000.9490.7590.5120.1690.0410.251
50-59세0.9760.9740.9700.8890.9410.9680.9230.9491.0000.8580.6680.3480.1920.334
60-69세0.8260.8330.8070.7220.7350.8350.7890.7590.8581.0000.8760.6750.5370.469
70-79세0.5950.6020.5780.4430.5170.6550.5530.5120.6680.8761.0000.8920.7410.497
80-89세0.2640.2780.2380.1150.1590.3290.2290.1690.3480.6750.8921.0000.9210.553
90-99세0.1420.1600.1080.0170.0020.1870.1250.0410.1920.5370.7410.9211.0000.572
100세 이상0.3250.3100.3140.2210.1910.2670.2610.2510.3340.4690.4970.5530.5721.000

Missing values

2023-12-12T15:34:32.441585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:34:32.716763image/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

행정구역인구수남자인구수여자인구수0-9세10-19세20-29세30-39세40-49세50-59세60-69세70-79세80-89세90-99세100세 이상
0봉담읍8806844604434647765104171109512270165181385797014263191325514
1우정읍175229619790370613851766156125583613321016469271464
2향남읍86494464264006888901073499851380618116123327549325615952283
3남양읍5101527722232934575428966598903843078816379253011681929
4매송면68153634318119942771059381513511486733445551
5비봉면70003980302023934077778079612441526740459972
6마도면69534180277318835066375393914411456683417612
7송산면109545994496036772593190013022110237512788001624
8서신면720840033205143287510539727149219249165601082
9팔탄면1002761103917281435921968138122342253901562892
행정구역인구수남자인구수여자인구수0-9세10-19세20-29세30-39세40-49세50-59세60-69세70-79세80-89세90-99세100세 이상
18기배동16257826179961377194821071973300928621769855310452
19화산동23819126001121914092138326629643921434435071511667920
20동탄1동50518261412437751846731802489439856733028691083445503
21동탄2동34560169881757231626591380933298254542723681104473421
22동탄3동41016211741984245475480619273478474479423731237497741
23동탄4동52977263052667273819921431272471388366812394820293414
24동탄5동460772308222995571048595251105369266516632851375550745
25동탄6동4202421639203855414475660738685857050362304795334561
26동탄7동9279646582462141428411578871219186207271012556211840634872
27동탄8동3325116740165115835341927767889717431992042642233411