Overview

Dataset statistics

Number of variables9
Number of observations1395
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.8 KiB
Average record size in memory79.1 B

Variable types

Categorical2
Text1
Numeric6

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 다자녀 가구수(가구), 1자녀(가구), 2자녀(가구), 3자녀(가구), 4자녀(가구), 5자녀(가구)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15110114/fileData.do

Alerts

다자녀 가구수(가구) is highly overall correlated with 1자녀(가구) and 4 other fieldsHigh correlation
1자녀(가구) is highly overall correlated with 다자녀 가구수(가구) and 4 other fieldsHigh correlation
2자녀(가구) is highly overall correlated with 다자녀 가구수(가구) and 4 other fieldsHigh correlation
3자녀(가구) is highly overall correlated with 다자녀 가구수(가구) and 4 other fieldsHigh correlation
4자녀(가구) is highly overall correlated with 다자녀 가구수(가구) and 4 other fieldsHigh correlation
5자녀(가구) is highly overall correlated with 다자녀 가구수(가구) and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 23:48:14.794827
Analysis finished2023-12-12 23:48:18.040410
Duration3.25 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
2017
286 
2018
283 
2020
277 
2019
276 
2021
273 

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 (%)
2017 286
20.5%
2018 283
20.3%
2020 277
19.9%
2019 276
19.8%
2021 273
19.6%

Length

2023-12-13T08:48:18.091061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:48:18.170480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 286
20.5%
2018 283
20.3%
2020 277
19.9%
2019 276
19.8%
2021 273
19.6%

시도명
Categorical

Distinct17
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
경기도
252 
경상남도
126 
서울특별시
124 
전라남도
119 
충청남도
100 
Other values (12)
674 

Length

Max length7
Median length5
Mean length4.1304659
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 252
18.1%
경상남도 126
9.0%
서울특별시 124
8.9%
전라남도 119
8.5%
충청남도 100
 
7.2%
경상북도 99
 
7.1%
부산광역시 87
 
6.2%
강원도 87
 
6.2%
충청북도 86
 
6.2%
전라북도 85
 
6.1%
Other values (7) 230
16.5%

Length

2023-12-13T08:48:18.268804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 252
18.1%
경상남도 126
9.0%
서울특별시 124
8.9%
전라남도 119
8.5%
충청남도 100
 
7.2%
경상북도 99
 
7.1%
부산광역시 87
 
6.2%
강원도 87
 
6.2%
충청북도 86
 
6.2%
전라북도 85
 
6.1%
Other values (7) 230
16.5%

시군구명
Text

Distinct232
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2023-12-13T08:48:18.540104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.827957
Min length2

Characters and Unicode

Total characters3945
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

Unique4 ?
Unique (%)0.3%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동부 70
 
5.0%
읍부 67
 
4.8%
면부 57
 
4.1%
동구 27
 
1.9%
남구 26
 
1.9%
북구 25
 
1.8%
서구 25
 
1.8%
중구 22
 
1.6%
강서구 10
 
0.7%
고성군 6
 
0.4%
Other values (222) 1060
76.0%
2023-12-13T08:48:18.935430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 514
 
13.0%
시 386
 
9.8%
군 338
 
8.6%
부 222
 
5.6%
동 165
 
4.2%
산 109
 
2.8%
천 105
 
2.7%
주 94
 
2.4%
성 83
 
2.1%
서 80
 
2.0%
Other values (132) 1849
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3945
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 514
 
13.0%
시 386
 
9.8%
군 338
 
8.6%
부 222
 
5.6%
동 165
 
4.2%
산 109
 
2.8%
천 105
 
2.7%
주 94
 
2.4%
성 83
 
2.1%
서 80
 
2.0%
Other values (132) 1849
46.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3945
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 514
 
13.0%
시 386
 
9.8%
군 338
 
8.6%
부 222
 
5.6%
동 165
 
4.2%
산 109
 
2.8%
천 105
 
2.7%
주 94
 
2.4%
성 83
 
2.1%
서 80
 
2.0%
Other values (132) 1849
46.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3945
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 514
 
13.0%
시 386
 
9.8%
군 338
 
8.6%
부 222
 
5.6%
동 165
 
4.2%
산 109
 
2.8%
천 105
 
2.7%
주 94
 
2.4%
성 83
 
2.1%
서 80
 
2.0%
Other values (132) 1849
46.9%

다자녀 가구수(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1233
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3694.0903
Minimum107
Maximum121528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T08:48:19.118297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile295.7
Q1827
median2311
Q34010
95-th percentile10869.7
Maximum121528
Range121421
Interquartile range (IQR)3183

Descriptive statistics

Standard deviation7850.4838
Coefficient of variation (CV)2.1251467
Kurtosis153.99057
Mean3694.0903
Median Absolute Deviation (MAD)1554
Skewness11.141131
Sum5153256
Variance61630097
MonotonicityNot monotonic
2023-12-13T08:48:19.318940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374 4
 
0.3%
953 3
 
0.2%
1524 3
 
0.2%
403 3
 
0.2%
508 3
 
0.2%
667 3
 
0.2%
428 3
 
0.2%
2557 3
 
0.2%
519 3
 
0.2%
633 3
 
0.2%
Other values (1223) 1364
97.8%
ValueCountFrequency (%)
107 1
0.1%
148 1
0.1%
153 1
0.1%
157 1
0.1%
163 1
0.1%
169 1
0.1%
174 1
0.1%
176 1
0.1%
187 1
0.1%
192 1
0.1%
ValueCountFrequency (%)
121528 1
0.1%
119248 1
0.1%
115673 1
0.1%
115033 1
0.1%
112552 1
0.1%
31860 1
0.1%
30726 1
0.1%
29289 1
0.1%
28628 1
0.1%
27568 1
0.1%

1자녀(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14250.576
Minimum401
Maximum497831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T08:48:19.447919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum401
5-th percentile835.1
Q12842.5
median8741
Q316404
95-th percentile41645.5
Maximum497831
Range497430
Interquartile range (IQR)13561.5

Descriptive statistics

Standard deviation32659.555
Coefficient of variation (CV)2.2918059
Kurtosis163.86109
Mean14250.576
Median Absolute Deviation (MAD)6563
Skewness11.646832
Sum19879554
Variance1.0666465 × 109
MonotonicityNot monotonic
2023-12-13T08:48:19.581263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1067 3
 
0.2%
3397 2
 
0.1%
918 2
 
0.1%
706 2
 
0.1%
14250 2
 
0.1%
867 2
 
0.1%
8184 2
 
0.1%
10141 2
 
0.1%
1178 2
 
0.1%
10380 2
 
0.1%
Other values (1339) 1374
98.5%
ValueCountFrequency (%)
401 1
0.1%
507 1
0.1%
523 1
0.1%
542 1
0.1%
544 1
0.1%
553 1
0.1%
561 1
0.1%
567 1
0.1%
597 1
0.1%
608 1
0.1%
ValueCountFrequency (%)
497831 1
0.1%
497153 1
0.1%
491255 1
0.1%
489142 1
0.1%
487546 1
0.1%
125334 1
0.1%
124836 1
0.1%
123441 1
0.1%
122372 1
0.1%
121005 1
0.1%

2자녀(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17662.469
Minimum303
Maximum646958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T08:48:19.719089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum303
5-th percentile834.7
Q13252.5
median10913
Q319824
95-th percentile52263.7
Maximum646958
Range646655
Interquartile range (IQR)16571.5

Descriptive statistics

Standard deviation41090.202
Coefficient of variation (CV)2.3264133
Kurtosis163.6438
Mean17662.469
Median Absolute Deviation (MAD)8072
Skewness11.612395
Sum24639144
Variance1.6884047 × 109
MonotonicityNot monotonic
2023-12-13T08:48:19.837604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11803 3
 
0.2%
637 2
 
0.1%
909 2
 
0.1%
1535 2
 
0.1%
1521 2
 
0.1%
14729 2
 
0.1%
6116 2
 
0.1%
1016 2
 
0.1%
1066 2
 
0.1%
15273 2
 
0.1%
Other values (1339) 1374
98.5%
ValueCountFrequency (%)
303 1
0.1%
451 1
0.1%
464 1
0.1%
481 1
0.1%
521 1
0.1%
534 1
0.1%
535 1
0.1%
548 1
0.1%
553 1
0.1%
565 1
0.1%
ValueCountFrequency (%)
646958 1
0.1%
634211 1
0.1%
613580 1
0.1%
605905 1
0.1%
591469 1
0.1%
158226 1
0.1%
153715 1
0.1%
151684 1
0.1%
145736 1
0.1%
144407 1
0.1%

3자녀(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1199
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3382.0323
Minimum90
Maximum112515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T08:48:20.235267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile253
Q1743.5
median2107
Q33674
95-th percentile9997.5
Maximum112515
Range112425
Interquartile range (IQR)2930.5

Descriptive statistics

Standard deviation7249.0872
Coefficient of variation (CV)2.1434116
Kurtosis154.66113
Mean3382.0323
Median Absolute Deviation (MAD)1442
Skewness11.173753
Sum4717935
Variance52549265
MonotonicityNot monotonic
2023-12-13T08:48:20.363667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311 4
 
0.3%
3216 4
 
0.3%
508 4
 
0.3%
2448 4
 
0.3%
500 3
 
0.2%
2244 3
 
0.2%
2090 3
 
0.2%
283 3
 
0.2%
2777 3
 
0.2%
480 3
 
0.2%
Other values (1189) 1361
97.6%
ValueCountFrequency (%)
90 1
0.1%
131 1
0.1%
135 1
0.1%
139 1
0.1%
143 1
0.1%
144 1
0.1%
149 1
0.1%
152 1
0.1%
160 1
0.1%
164 1
0.1%
ValueCountFrequency (%)
112515 1
0.1%
110279 1
0.1%
106766 1
0.1%
106233 1
0.1%
103859 1
0.1%
29320 1
0.1%
28238 1
0.1%
26918 1
0.1%
26270 1
0.1%
25314 1
0.1%

4자녀(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct534
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.14409
Minimum12
Maximum8057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T08:48:20.506117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile33
Q176
median157
Q3289
95-th percentile825.9
Maximum8057
Range8045
Interquartile range (IQR)213

Descriptive statistics

Standard deviation539.01713
Coefficient of variation (CV)1.9733802
Kurtosis144.51695
Mean273.14409
Median Absolute Deviation (MAD)98
Skewness10.673704
Sum381036
Variance290539.46
MonotonicityNot monotonic
2023-12-13T08:48:20.651155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 16
 
1.1%
59 11
 
0.8%
44 11
 
0.8%
102 11
 
0.8%
60 10
 
0.7%
54 10
 
0.7%
51 10
 
0.7%
50 10
 
0.7%
36 10
 
0.7%
34 10
 
0.7%
Other values (524) 1286
92.2%
ValueCountFrequency (%)
12 2
 
0.1%
13 2
 
0.1%
14 1
 
0.1%
16 1
 
0.1%
18 1
 
0.1%
19 3
0.2%
20 2
 
0.1%
21 5
0.4%
22 2
 
0.1%
23 5
0.4%
ValueCountFrequency (%)
8057 1
0.1%
8008 1
0.1%
7948 1
0.1%
7854 1
0.1%
7759 1
0.1%
2227 1
0.1%
2173 1
0.1%
2067 1
0.1%
2061 1
0.1%
1974 1
0.1%

5자녀(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.913978
Minimum5
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T08:48:20.816247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q113
median23
Q340
95-th percentile120.3
Maximum961
Range956
Interquartile range (IQR)27

Descriptive statistics

Standard deviation67.809377
Coefficient of variation (CV)1.7425455
Kurtosis116.87529
Mean38.913978
Median Absolute Deviation (MAD)12
Skewness9.338279
Sum54285
Variance4598.1117
MonotonicityNot monotonic
2023-12-13T08:48:20.943465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 50
 
3.6%
14 48
 
3.4%
10 46
 
3.3%
8 45
 
3.2%
11 42
 
3.0%
12 41
 
2.9%
9 41
 
2.9%
5 39
 
2.8%
16 39
 
2.8%
7 39
 
2.8%
Other values (148) 965
69.2%
ValueCountFrequency (%)
5 39
2.8%
6 50
3.6%
7 39
2.8%
8 45
3.2%
9 41
2.9%
10 46
3.3%
11 42
3.0%
12 41
2.9%
13 33
2.4%
14 48
3.4%
ValueCountFrequency (%)
961 1
0.1%
959 1
0.1%
956 1
0.1%
946 1
0.1%
934 1
0.1%
315 1
0.1%
313 1
0.1%
304 1
0.1%
297 1
0.1%
280 1
0.1%

Interactions

2023-12-13T08:48:17.409930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.242739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.676001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.094445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.532970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.964541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.479975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.313224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.746860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.166933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.604156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.040943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.549236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.383418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.817201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.233310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.674310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.114691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.617910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.455828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.886116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.308694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.750759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.189459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.684875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.524249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.952933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.382181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.814542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.260738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.758717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:15.602165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.025948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.466038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:16.893059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:17.337323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:48:21.052074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명다자녀 가구수(가구)1자녀(가구)2자녀(가구)3자녀(가구)4자녀(가구)5자녀(가구)
통계연도1.0000.0000.0000.0000.0000.0000.0000.000
시도명0.0001.0000.2470.2920.2700.2470.3440.367
다자녀 가구수(가구)0.0000.2471.0000.9850.9921.0000.9860.835
1자녀(가구)0.0000.2920.9851.0000.9980.9850.9760.816
2자녀(가구)0.0000.2700.9920.9981.0000.9920.9770.814
3자녀(가구)0.0000.2471.0000.9850.9921.0000.9860.835
4자녀(가구)0.0000.3440.9860.9760.9770.9861.0000.877
5자녀(가구)0.0000.3670.8350.8160.8140.8350.8771.000
2023-12-13T08:48:21.178218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-13T08:48:21.272180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
다자녀 가구수(가구)1자녀(가구)2자녀(가구)3자녀(가구)4자녀(가구)5자녀(가구)통계연도시도명
다자녀 가구수(가구)1.0000.9670.9761.0000.9790.8880.0000.139
1자녀(가구)0.9671.0000.9950.9700.9160.8010.0000.165
2자녀(가구)0.9760.9951.0000.9790.9210.8040.0000.152
3자녀(가구)1.0000.9700.9791.0000.9740.8800.0000.139
4자녀(가구)0.9790.9160.9210.9741.0000.9340.0000.197
5자녀(가구)0.8880.8010.8040.8800.9341.0000.0000.197
통계연도0.0000.0000.0000.0000.0000.0001.0000.000
시도명0.1390.1650.1520.1390.1970.1970.0001.000

Missing values

2023-12-13T08:48:17.873383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:48:17.992422image/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

통계연도시도명시군구명다자녀 가구수(가구)1자녀(가구)2자녀(가구)3자녀(가구)4자녀(가구)5자녀(가구)
02017서울특별시종로구11825261561910748919
12017서울특별시중구785431440767086710
22017서울특별시용산구156786848271144211510
32017서울특별시성동구19791298313013180915218
42017서울특별시광진구24891406915502231015623
52017서울특별시동대문구26261257614476239320726
62017서울특별시중랑구34771503817007318525339
72017서울특별시성북구37371794521520346823831
82017서울특별시강북구26681152913281244018741
92017서울특별시도봉구28001337816093257519728
통계연도시도명시군구명다자녀 가구수(가구)1자녀(가구)2자녀(가구)3자녀(가구)4자녀(가구)5자녀(가구)
13852021경상남도하동군324944928292275
13862021경상남도산청군241709658195379
13872021경상남도함양군3559041025311386
13882021경상남도거창군707176721756306512
13892021경상남도합천군2757888402293511
13902021제주특별자치도동부886819193252197942820106
13912021제주특별자치도읍부231755835900199328440
13922021제주특별자치도면부460110111043856213
13932021제주특별자치도제주시881519086247207832872111
13942021제주특별자치도서귀포시283067917503248829448