Overview

Dataset statistics

Number of variables10
Number of observations1394
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.9 KiB
Average record size in memory88.1 B

Variable types

Categorical2
Text1
Numeric7

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 합계출산율(퍼센트), 20에서 24세(명), 25에서 29세(명), 30에서 34세(명), 35에서 39세(명), 40에서 44세(명), 45에서 49세(명)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110131

Alerts

합계출산율(퍼센트) is highly overall correlated with 20에서 24세(명) and 3 other fieldsHigh correlation
20에서 24세(명) is highly overall correlated with 합계출산율(퍼센트) and 2 other fieldsHigh correlation
25에서 29세(명) is highly overall correlated with 합계출산율(퍼센트) and 2 other fieldsHigh correlation
30에서 34세(명) is highly overall correlated with 합계출산율(퍼센트) and 3 other fieldsHigh correlation
35에서 39세(명) is highly overall correlated with 합계출산율(퍼센트) and 1 other fieldsHigh correlation
합계출산율(퍼센트) has 94 (6.7%) zerosZeros
20에서 24세(명) has 96 (6.9%) zerosZeros
25에서 29세(명) has 94 (6.7%) zerosZeros
30에서 34세(명) has 94 (6.7%) zerosZeros
35에서 39세(명) has 94 (6.7%) zerosZeros
40에서 44세(명) has 99 (7.1%) zerosZeros
45에서 49세(명) has 595 (42.7%) zerosZeros

Reproduction

Analysis started2023-12-10 22:47:51.670308
Analysis finished2023-12-10 22:47:57.189964
Duration5.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2018
279 
2021
279 
2019
279 
2020
279 
2017
278 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2018 279
20.0%
2021 279
20.0%
2019 279
20.0%
2020 279
20.0%
2017 278
19.9%

Length

2023-12-11T07:47:57.248315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:47:57.351324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 279
20.0%
2021 279
20.0%
2019 279
20.0%
2020 279
20.0%
2017 278
19.9%

시도명
Categorical

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
경기도
285 
경상남도
135 
서울특별시
125 
경상북도
125 
전라남도
110 
Other values (11)
614 

Length

Max length7
Median length5
Mean length4.0423242
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 285
20.4%
경상남도 135
9.7%
서울특별시 125
9.0%
경상북도 125
9.0%
전라남도 110
 
7.9%
충청남도 95
 
6.8%
강원도 90
 
6.5%
부산광역시 80
 
5.7%
충청북도 80
 
5.7%
전라북도 80
 
5.7%
Other values (6) 189
13.6%

Length

2023-12-11T07:47:57.481325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 285
20.4%
경상남도 135
9.7%
서울특별시 125
9.0%
경상북도 125
9.0%
전라남도 110
 
7.9%
충청남도 95
 
6.8%
강원도 90
 
6.5%
부산광역시 80
 
5.7%
충청북도 80
 
5.7%
전라북도 80
 
5.7%
Other values (6) 189
13.6%

시군구명
Text

Distinct254
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2023-12-11T07:47:57.781833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9634146
Min length2

Characters and Unicode

Total characters4131
Distinct characters142
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 30
 
2.2%
남구 30
 
2.2%
동구 30
 
2.2%
북구 25
 
1.8%
서구 25
 
1.8%
고성군 10
 
0.7%
강서구 10
 
0.7%
정읍시 5
 
0.4%
덕진구 5
 
0.4%
군산시 5
 
0.4%
Other values (244) 1219
87.4%
2023-12-11T07:47:58.242379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 564
 
13.7%
군 475
 
11.5%
시 400
 
9.7%
주 125
 
3.0%
산 120
 
2.9%
천 115
 
2.8%
동 100
 
2.4%
양 100
 
2.4%
성 100
 
2.4%
남 80
 
1.9%
Other values (132) 1952
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4131
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 564
 
13.7%
군 475
 
11.5%
시 400
 
9.7%
주 125
 
3.0%
산 120
 
2.9%
천 115
 
2.8%
동 100
 
2.4%
양 100
 
2.4%
성 100
 
2.4%
남 80
 
1.9%
Other values (132) 1952
47.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4131
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 564
 
13.7%
군 475
 
11.5%
시 400
 
9.7%
주 125
 
3.0%
산 120
 
2.9%
천 115
 
2.8%
동 100
 
2.4%
양 100
 
2.4%
성 100
 
2.4%
남 80
 
1.9%
Other values (132) 1952
47.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4131
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 564
 
13.7%
군 475
 
11.5%
시 400
 
9.7%
주 125
 
3.0%
산 120
 
2.9%
천 115
 
2.8%
동 100
 
2.4%
양 100
 
2.4%
성 100
 
2.4%
남 80
 
1.9%
Other values (132) 1952
47.3%

합계출산율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct135
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95246772
Minimum0
Maximum2.54
Zeros94
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:58.387901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.81
median0.97
Q31.14
95-th percentile1.52
Maximum2.54
Range2.54
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.3595827
Coefficient of variation (CV)0.37752744
Kurtosis1.9684527
Mean0.95246772
Median Absolute Deviation (MAD)0.17
Skewness-0.6289771
Sum1327.74
Variance0.12929972
MonotonicityNot monotonic
2023-12-11T07:47:58.526016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 94
 
6.7%
1.01 31
 
2.2%
0.98 29
 
2.1%
1.0 28
 
2.0%
0.91 28
 
2.0%
1.05 27
 
1.9%
0.88 27
 
1.9%
0.95 26
 
1.9%
0.99 26
 
1.9%
1.18 25
 
1.8%
Other values (125) 1053
75.5%
ValueCountFrequency (%)
0.0 94
6.7%
0.38 1
 
0.1%
0.44 1
 
0.1%
0.45 1
 
0.1%
0.47 2
 
0.1%
0.5 2
 
0.1%
0.52 2
 
0.1%
0.53 3
 
0.2%
0.54 4
 
0.3%
0.55 3
 
0.2%
ValueCountFrequency (%)
2.54 1
0.1%
2.46 1
0.1%
2.1 1
0.1%
1.89 2
0.1%
1.87 1
0.1%
1.83 2
0.1%
1.82 2
0.1%
1.8 2
0.1%
1.78 2
0.1%
1.77 1
0.1%

20에서 24세(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct249
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1104017
Minimum0
Maximum44.9
Zeros96
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:58.680229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median8.4
Q312.7
95-th percentile20.6
Maximum44.9
Range44.9
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation6.1832915
Coefficient of variation (CV)0.67870679
Kurtosis1.3350907
Mean9.1104017
Median Absolute Deviation (MAD)4
Skewness0.90176565
Sum12699.9
Variance38.233093
MonotonicityNot monotonic
2023-12-11T07:47:58.821260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 96
 
6.9%
8.6 14
 
1.0%
7.8 14
 
1.0%
6.6 14
 
1.0%
9.4 14
 
1.0%
4.3 14
 
1.0%
5.2 13
 
0.9%
8.5 13
 
0.9%
10.2 13
 
0.9%
9.5 13
 
0.9%
Other values (239) 1176
84.4%
ValueCountFrequency (%)
0.0 96
6.9%
0.3 1
 
0.1%
0.4 1
 
0.1%
0.6 2
 
0.1%
0.7 1
 
0.1%
0.9 1
 
0.1%
1.0 6
 
0.4%
1.1 1
 
0.1%
1.2 2
 
0.1%
1.3 3
 
0.2%
ValueCountFrequency (%)
44.9 1
0.1%
38.3 1
0.1%
34.0 1
0.1%
31.5 1
0.1%
30.3 1
0.1%
29.7 1
0.1%
29.0 1
0.1%
28.7 1
0.1%
28.5 1
0.1%
28.3 1
0.1%

25에서 29세(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct626
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.088522
Minimum0
Maximum131.3
Zeros94
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:58.945308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127.2
median42.05
Q357.3
95-th percentile85.2
Maximum131.3
Range131.3
Interquartile range (IQR)30.1

Descriptive statistics

Standard deviation23.518209
Coefficient of variation (CV)0.54581145
Kurtosis0.33537921
Mean43.088522
Median Absolute Deviation (MAD)15.05
Skewness0.40329209
Sum60065.4
Variance553.10614
MonotonicityNot monotonic
2023-12-11T07:47:59.084111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 94
 
6.7%
39.8 8
 
0.6%
42.3 8
 
0.6%
38.2 8
 
0.6%
37.2 7
 
0.5%
37.8 6
 
0.4%
34.6 6
 
0.4%
31.2 6
 
0.4%
31.9 6
 
0.4%
40.6 6
 
0.4%
Other values (616) 1239
88.9%
ValueCountFrequency (%)
0.0 94
6.7%
6.7 1
 
0.1%
7.2 1
 
0.1%
8.4 2
 
0.1%
8.7 1
 
0.1%
8.9 1
 
0.1%
9.3 1
 
0.1%
9.4 1
 
0.1%
9.6 1
 
0.1%
10.5 2
 
0.1%
ValueCountFrequency (%)
131.3 1
0.1%
126.6 1
0.1%
116.7 1
0.1%
116.3 1
0.1%
115.1 1
0.1%
111.9 1
0.1%
110.9 2
0.1%
110.8 1
0.1%
110.5 1
0.1%
110.4 1
0.1%

30에서 34세(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct644
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.405524
Minimum0
Maximum250.8
Zeros94
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:59.230829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173.8
median88.5
Q3101.5
95-th percentile129.205
Maximum250.8
Range250.8
Interquartile range (IQR)27.7

Descriptive statistics

Standard deviation31.710232
Coefficient of variation (CV)0.37129018
Kurtosis2.6103833
Mean85.405524
Median Absolute Deviation (MAD)14
Skewness-0.67629601
Sum119055.3
Variance1005.5388
MonotonicityNot monotonic
2023-12-11T07:47:59.372345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 94
 
6.7%
93.2 7
 
0.5%
78.0 7
 
0.5%
87.7 7
 
0.5%
75.7 6
 
0.4%
76.8 6
 
0.4%
87.2 6
 
0.4%
89.2 6
 
0.4%
96.5 6
 
0.4%
88.2 6
 
0.4%
Other values (634) 1243
89.2%
ValueCountFrequency (%)
0.0 94
6.7%
33.7 1
 
0.1%
36.1 1
 
0.1%
36.9 1
 
0.1%
37.6 1
 
0.1%
38.7 1
 
0.1%
41.2 1
 
0.1%
42.0 1
 
0.1%
43.3 1
 
0.1%
44.4 1
 
0.1%
ValueCountFrequency (%)
250.8 1
0.1%
232.9 1
0.1%
201.2 1
0.1%
188.8 1
0.1%
181.3 1
0.1%
175.9 1
0.1%
163.9 1
0.1%
162.7 1
0.1%
161.6 1
0.1%
159.4 1
0.1%

35에서 39세(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct369
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.111549
Minimum0
Maximum111.1
Zeros94
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:59.498805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138.9
median44.1
Q349.475
95-th percentile64.635
Maximum111.1
Range111.1
Interquartile range (IQR)10.575

Descriptive statistics

Standard deviation15.039713
Coefficient of variation (CV)0.34885578
Kurtosis3.4437337
Mean43.111549
Median Absolute Deviation (MAD)5.25
Skewness-0.90378799
Sum60097.5
Variance226.19297
MonotonicityNot monotonic
2023-12-11T07:47:59.620980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 94
 
6.7%
46.8 15
 
1.1%
44.1 14
 
1.0%
41.6 13
 
0.9%
42.9 13
 
0.9%
46.4 12
 
0.9%
47.5 11
 
0.8%
42.4 11
 
0.8%
48.5 11
 
0.8%
44.3 10
 
0.7%
Other values (359) 1190
85.4%
ValueCountFrequency (%)
0.0 94
6.7%
17.1 1
 
0.1%
21.5 1
 
0.1%
23.1 1
 
0.1%
23.5 1
 
0.1%
23.7 1
 
0.1%
23.9 1
 
0.1%
24.3 1
 
0.1%
25.2 1
 
0.1%
26.2 1
 
0.1%
ValueCountFrequency (%)
111.1 1
0.1%
98.2 1
0.1%
97.3 1
0.1%
92.6 1
0.1%
90.6 1
0.1%
90.3 1
0.1%
89.7 1
0.1%
88.7 1
0.1%
85.8 1
0.1%
85.6 1
0.1%

40에서 44세(명)
Real number (ℝ)

ZEROS 

Distinct133
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6440459
Minimum0
Maximum21.6
Zeros99
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:59.746872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4
median6.7
Q37.9
95-th percentile11.5
Maximum21.6
Range21.6
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.8984853
Coefficient of variation (CV)0.43625306
Kurtosis2.325296
Mean6.6440459
Median Absolute Deviation (MAD)1.2
Skewness0.1586475
Sum9261.8
Variance8.4012172
MonotonicityNot monotonic
2023-12-11T07:47:59.886184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 99
 
7.1%
6.0 39
 
2.8%
6.3 38
 
2.7%
6.7 36
 
2.6%
7.5 35
 
2.5%
6.2 33
 
2.4%
5.8 33
 
2.4%
7.0 32
 
2.3%
6.9 32
 
2.3%
7.1 31
 
2.2%
Other values (123) 986
70.7%
ValueCountFrequency (%)
0.0 99
7.1%
1.1 1
 
0.1%
1.2 1
 
0.1%
1.7 2
 
0.1%
1.9 1
 
0.1%
2.1 1
 
0.1%
2.2 1
 
0.1%
2.3 2
 
0.1%
2.4 2
 
0.1%
2.8 2
 
0.1%
ValueCountFrequency (%)
21.6 1
0.1%
19.1 1
0.1%
18.8 1
0.1%
18.2 1
0.1%
17.3 1
0.1%
17.0 1
0.1%
16.7 1
0.1%
16.6 2
0.1%
16.3 1
0.1%
16.0 1
0.1%

45에서 49세(명)
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17109039
Minimum0
Maximum3.5
Zeros595
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-11T07:47:59.992049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q30.2
95-th percentile0.6
Maximum3.5
Range3.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.28750598
Coefficient of variation (CV)1.6804332
Kurtosis30.327591
Mean0.17109039
Median Absolute Deviation (MAD)0.1
Skewness4.4157506
Sum238.5
Variance0.082659686
MonotonicityNot monotonic
2023-12-11T07:48:00.120331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 595
42.7%
0.1 258
18.5%
0.2 227
 
16.3%
0.3 153
 
11.0%
0.4 46
 
3.3%
0.5 26
 
1.9%
0.6 21
 
1.5%
0.7 19
 
1.4%
0.8 10
 
0.7%
1.2 6
 
0.4%
Other values (12) 33
 
2.4%
ValueCountFrequency (%)
0.0 595
42.7%
0.1 258
18.5%
0.2 227
 
16.3%
0.3 153
 
11.0%
0.4 46
 
3.3%
0.5 26
 
1.9%
0.6 21
 
1.5%
0.7 19
 
1.4%
0.8 10
 
0.7%
0.9 5
 
0.4%
ValueCountFrequency (%)
3.5 1
 
0.1%
2.6 1
 
0.1%
2.5 2
 
0.1%
2.3 2
 
0.1%
1.7 1
 
0.1%
1.6 3
0.2%
1.5 4
0.3%
1.4 2
 
0.1%
1.3 3
0.2%
1.2 6
0.4%

Interactions

2023-12-11T07:47:56.092129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.251630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.833942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.435917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.059941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.762237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.454469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:56.188396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.341331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.908861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.515840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.145123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.844956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.549627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:56.277425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.422405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.980555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.597629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.232043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.937025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.627766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:56.385109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.510153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.058232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.678552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.339397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.048556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.751888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:56.468274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.596427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.145226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.754757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.429375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.144536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.831037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:56.771023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.675582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.242952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.841593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.580348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.242858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.912652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:56.869913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:52.748855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.330472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:53.943027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:54.664928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.342765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:55.985537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:48:00.212974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명합계출산율(퍼센트)20에서 24세(명)25에서 29세(명)30에서 34세(명)35에서 39세(명)40에서 44세(명)45에서 49세(명)
통계연도1.0000.0000.3750.3200.3880.3770.2210.3090.044
시도명0.0001.0000.5780.5240.5790.5120.4490.3750.230
합계출산율(퍼센트)0.3750.5781.0000.7900.9010.9690.9030.8080.213
20에서 24세(명)0.3200.5240.7901.0000.8170.6560.5520.4670.219
25에서 29세(명)0.3880.5790.9010.8171.0000.8380.7570.7030.189
30에서 34세(명)0.3770.5120.9690.6560.8381.0000.9060.8020.150
35에서 39세(명)0.2210.4490.9030.5520.7570.9061.0000.8340.215
40에서 44세(명)0.3090.3750.8080.4670.7030.8020.8341.0000.219
45에서 49세(명)0.0440.2300.2130.2190.1890.1500.2150.2191.000
2023-12-11T07:48:00.335761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T07:48:00.421682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계출산율(퍼센트)20에서 24세(명)25에서 29세(명)30에서 34세(명)35에서 39세(명)40에서 44세(명)45에서 49세(명)통계연도시도명
합계출산율(퍼센트)1.0000.8110.9310.9300.6330.2590.0400.1630.268
20에서 24세(명)0.8111.0000.8590.6370.3440.1940.0160.1380.235
25에서 29세(명)0.9310.8591.0000.7900.4320.1740.0310.1710.270
30에서 34세(명)0.9300.6370.7901.0000.6220.2200.0690.1650.228
35에서 39세(명)0.6330.3440.4320.6221.0000.4290.1040.0930.193
40에서 44세(명)0.2590.1940.1740.2200.4291.0000.1030.1320.156
45에서 49세(명)0.0400.0160.0310.0690.1040.1031.0000.0360.080
통계연도0.1630.1380.1710.1650.0930.1320.0361.0000.000
시도명0.2680.2350.2700.2280.1930.1560.0800.0001.000

Missing values

2023-12-11T07:47:57.010567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:47:57.138182image/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

통계연도시도명시군구명합계출산율(퍼센트)20에서 24세(명)25에서 29세(명)30에서 34세(명)35에서 39세(명)40에서 44세(명)45에서 49세(명)
02017서울특별시종로구0.651.920.058.540.37.80.0
12017서울특별시중구0.824.725.575.551.27.70.2
22017서울특별시용산구0.793.823.773.250.47.50.3
32017서울특별시성동구0.974.830.794.957.97.40.2
42017서울특별시광진구0.752.924.373.344.65.40.2
52017서울특별시동대문구0.824.124.279.749.56.40.1
62017서울특별시중랑구0.918.633.579.553.16.30.2
72017서울특별시성북구0.823.625.979.346.87.20.1
82017서울특별시강북구0.836.628.376.143.97.90.1
92017서울특별시도봉구0.835.530.780.744.04.90.3
통계연도시도명시군구명합계출산율(퍼센트)20에서 24세(명)25에서 29세(명)30에서 34세(명)35에서 39세(명)40에서 44세(명)45에서 49세(명)
13842021경상남도남해군0.817.234.662.941.09.32.5
13852021경상남도하동군1.136.946.0107.053.86.10.0
13862021경상남도산청군0.997.031.8101.633.617.30.0
13872021경상남도함양군0.895.645.583.731.26.00.0
13882021경상남도거창군0.928.145.676.039.410.20.0
13892021경상남도합천군0.8111.634.060.141.89.10.0
13902021제주특별자치도제주시0.978.839.184.448.210.40.4
13912021제주특별자치도서귀포시0.899.835.773.145.711.60.3
13922021제주특별자치도북제주군0.00.00.00.00.00.00.0
13932021제주특별자치도남제주군0.00.00.00.00.00.00.0