Overview

Dataset statistics

Number of variables10
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory87.0 B

Variable types

Numeric4
Categorical2
Text4

Dataset

Description2024년도 경기도 고양시 의료급여 대상자 현황 자료입니다[읍면동 젠체, 가수구, 수급권자수(종별 분리) 등]
Author경기도 고양시
URLhttps://www.data.go.kr/data/3078378/fileData.do

Alerts

시도 has constant value ""Constant
번호 is highly overall correlated with 시군구High correlation
가구수(1종) is highly overall correlated with 가구수(2종) and 1 other fieldsHigh correlation
가구수(2종) is highly overall correlated with 가구수(1종) and 1 other fieldsHigh correlation
수급권자수(2종) is highly overall correlated with 가구수(1종) and 1 other fieldsHigh correlation
시군구 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique
읍면동 has unique valuesUnique
가구수(합계) has unique valuesUnique

Reproduction

Analysis started2024-04-29 23:23:45.618110
Analysis finished2024-04-29 23:23:49.028795
Duration3.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-30T08:23:49.109850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q111.75
median22.5
Q333.25
95-th percentile41.85
Maximum44
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation12.845233
Coefficient of variation (CV)0.57089923
Kurtosis-1.2
Mean22.5
Median Absolute Deviation (MAD)11
Skewness0
Sum990
Variance165
MonotonicityStrictly increasing
2024-04-30T08:23:49.235379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 1
 
2.3%
24 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
33 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
44 1
2.3%
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
고양시
44 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 44
100.0%

Length

2024-04-30T08:23:49.348299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:23:49.446893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 44
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
덕양구
21 
일산동구
12 
일산서구
11 

Length

Max length4
Median length4
Mean length3.5227273
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
덕양구 21
47.7%
일산동구 12
27.3%
일산서구 11
25.0%

Length

2024-04-30T08:23:49.554187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T08:23:49.641666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
덕양구 21
47.7%
일산동구 12
27.3%
일산서구 11
25.0%

읍면동
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-04-30T08:23:49.830935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5909091
Min length3

Characters and Unicode

Total characters158
Distinct characters50
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

Unique44 ?
Unique (%)100.0%

Sample

1st row주교동
2nd row원신동
3rd row흥도동
4th row성사1동
5th row성사2동
ValueCountFrequency (%)
주교동 1
 
2.3%
원신동 1
 
2.3%
고봉동 1
 
2.3%
정발산동 1
 
2.3%
풍산동 1
 
2.3%
백석1동 1
 
2.3%
백석2동 1
 
2.3%
마두1동 1
 
2.3%
마두2동 1
 
2.3%
장항1동 1
 
2.3%
Other values (34) 34
77.3%
2024-04-30T08:23:50.143677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 44
27.8%
1 11
 
7.0%
2 11
 
7.0%
산 8
 
5.1%
행 5
 
3.2%
신 5
 
3.2%
주 4
 
2.5%
화 4
 
2.5%
사 3
 
1.9%
정 3
 
1.9%
Other values (40) 60
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
84.2%
Decimal Number 25
 
15.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
주 4
 
3.0%
화 4
 
3.0%
사 3
 
2.3%
정 3
 
2.3%
송 3
 
2.3%
일 3
 
2.3%
Other values (36) 51
38.3%
Decimal Number
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
84.2%
Common 25
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
주 4
 
3.0%
화 4
 
3.0%
사 3
 
2.3%
정 3
 
2.3%
송 3
 
2.3%
일 3
 
2.3%
Other values (36) 51
38.3%
Common
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
84.2%
ASCII 25
 
15.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
주 4
 
3.0%
화 4
 
3.0%
사 3
 
2.3%
정 3
 
2.3%
송 3
 
2.3%
일 3
 
2.3%
Other values (36) 51
38.3%
ASCII
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%

가구수(합계)
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-04-30T08:23:50.351195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters132
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row522
2nd row537
3rd row534
4th row599
5th row126
ValueCountFrequency (%)
522 1
 
2.3%
537 1
 
2.3%
828 1
 
2.3%
315 1
 
2.3%
429 1
 
2.3%
184 1
 
2.3%
956 1
 
2.3%
135 1
 
2.3%
99 1
 
2.3%
42 1
 
2.3%
Other values (34) 34
77.3%
2024-04-30T08:23:50.700583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
16.7%
2 15
11.4%
4 15
11.4%
5 14
10.6%
3 14
10.6%
6 13
9.8%
9 13
9.8%
8 10
7.6%
0 9
6.8%
7 5
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
98.5%
Other Punctuation 2
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
16.9%
2 15
11.5%
4 15
11.5%
5 14
10.8%
3 14
10.8%
6 13
10.0%
9 13
10.0%
8 10
7.7%
0 9
6.9%
7 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
16.7%
2 15
11.4%
4 15
11.4%
5 14
10.6%
3 14
10.6%
6 13
9.8%
9 13
9.8%
8 10
7.6%
0 9
6.8%
7 5
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
16.7%
2 15
11.4%
4 15
11.4%
5 14
10.6%
3 14
10.6%
6 13
9.8%
9 13
9.8%
8 10
7.6%
0 9
6.8%
7 5
 
3.8%

수급권자수(합계)
Text

Distinct42
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-04-30T08:23:50.875211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1136364
Min length2

Characters and Unicode

Total characters137
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)93.2%

Sample

1st row599
2nd row640
3rd row631
4th row676
5th row141
ValueCountFrequency (%)
128 3
 
6.8%
49 1
 
2.3%
87 1
 
2.3%
668 1
 
2.3%
390 1
 
2.3%
538 1
 
2.3%
226 1
 
2.3%
1,112 1
 
2.3%
179 1
 
2.3%
104 1
 
2.3%
Other values (32) 32
72.7%
2024-04-30T08:23:51.169613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
16.1%
2 16
11.7%
9 14
10.2%
6 14
10.2%
8 13
9.5%
3 12
8.8%
7 12
8.8%
4 11
8.0%
5 11
8.0%
0 8
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 133
97.1%
Other Punctuation 4
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
16.5%
2 16
12.0%
9 14
10.5%
6 14
10.5%
8 13
9.8%
3 12
9.0%
7 12
9.0%
4 11
8.3%
5 11
8.3%
0 8
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 137
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
16.1%
2 16
11.7%
9 14
10.2%
6 14
10.2%
8 13
9.5%
3 12
8.8%
7 12
8.8%
4 11
8.0%
5 11
8.0%
0 8
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
16.1%
2 16
11.7%
9 14
10.2%
6 14
10.2%
8 13
9.5%
3 12
8.8%
7 12
8.8%
4 11
8.0%
5 11
8.0%
0 8
 
5.8%

가구수(1종)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.86364
Minimum33
Maximum981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-30T08:23:51.312125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile63.9
Q1124.25
median261.5
Q3457.25
95-th percentile831.8
Maximum981
Range948
Interquartile range (IQR)333

Descriptive statistics

Standard deviation252.56817
Coefficient of variation (CV)0.74976382
Kurtosis-0.093510935
Mean336.86364
Median Absolute Deviation (MAD)156.5
Skewness0.92478804
Sum14822
Variance63790.679
MonotonicityNot monotonic
2024-04-30T08:23:51.438715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
105 2
 
4.5%
409 1
 
2.3%
376 1
 
2.3%
365 1
 
2.3%
149 1
 
2.3%
858 1
 
2.3%
86 1
 
2.3%
33 1
 
2.3%
266 1
 
2.3%
774 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
33 1
2.3%
51 1
2.3%
60 1
2.3%
86 1
2.3%
88 1
2.3%
90 1
2.3%
93 1
2.3%
105 2
4.5%
108 1
2.3%
119 1
2.3%
ValueCountFrequency (%)
981 1
2.3%
858 1
2.3%
842 1
2.3%
774 1
2.3%
724 1
2.3%
710 1
2.3%
703 1
2.3%
661 1
2.3%
549 1
2.3%
500 1
2.3%

수급권자수(1종)
Text

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-04-30T08:23:51.615535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9318182
Min length2

Characters and Unicode

Total characters129
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)95.5%

Sample

1st row438
2nd row481
3rd row527
4th row542
5th row117
ValueCountFrequency (%)
136 2
 
4.5%
409 1
 
2.3%
272 1
 
2.3%
412 1
 
2.3%
166 1
 
2.3%
964 1
 
2.3%
124 1
 
2.3%
88 1
 
2.3%
35 1
 
2.3%
291 1
 
2.3%
Other values (33) 33
75.0%
2024-04-30T08:23:51.926025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 23
17.8%
1 20
15.5%
9 13
10.1%
4 12
9.3%
8 12
9.3%
3 11
8.5%
0 11
8.5%
6 9
 
7.0%
5 9
 
7.0%
7 8
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
99.2%
Other Punctuation 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 23
18.0%
1 20
15.6%
9 13
10.2%
4 12
9.4%
8 12
9.4%
3 11
8.6%
0 11
8.6%
6 9
 
7.0%
5 9
 
7.0%
7 8
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 23
17.8%
1 20
15.5%
9 13
10.1%
4 12
9.3%
8 12
9.3%
3 11
8.5%
0 11
8.5%
6 9
 
7.0%
5 9
 
7.0%
7 8
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 23
17.8%
1 20
15.5%
9 13
10.1%
4 12
9.3%
8 12
9.3%
3 11
8.5%
0 11
8.5%
6 9
 
7.0%
5 9
 
7.0%
7 8
 
6.2%

가구수(2종)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.590909
Minimum9
Maximum221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-30T08:23:52.052329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile13
Q121.75
median51
Q387.25
95-th percentile123.2
Maximum221
Range212
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation43.232373
Coefficient of variation (CV)0.73786827
Kurtosis3.0108487
Mean58.590909
Median Absolute Deviation (MAD)31.5
Skewness1.3620846
Sum2578
Variance1869.0381
MonotonicityNot monotonic
2024-04-30T08:23:52.168793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
35 3
 
6.8%
19 2
 
4.5%
15 2
 
4.5%
64 2
 
4.5%
98 2
 
4.5%
13 2
 
4.5%
86 2
 
4.5%
9 1
 
2.3%
79 1
 
2.3%
58 1
 
2.3%
Other values (26) 26
59.1%
ValueCountFrequency (%)
9 1
2.3%
11 1
2.3%
13 2
4.5%
15 2
4.5%
17 1
2.3%
19 2
4.5%
20 1
2.3%
21 1
2.3%
22 1
2.3%
24 1
2.3%
ValueCountFrequency (%)
221 1
2.3%
126 1
2.3%
125 1
2.3%
113 1
2.3%
111 1
2.3%
106 1
2.3%
99 1
2.3%
98 2
4.5%
94 1
2.3%
91 1
2.3%

수급권자수(2종)
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.181818
Minimum14
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-30T08:23:52.282052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile18.45
Q137.75
median74
Q3135.25
95-th percentile183.25
Maximum333
Range319
Interquartile range (IQR)97.5

Descriptive statistics

Standard deviation64.99813
Coefficient of variation (CV)0.72882714
Kurtosis2.9994634
Mean89.181818
Median Absolute Deviation (MAD)45.5
Skewness1.3539307
Sum3924
Variance4224.7569
MonotonicityNot monotonic
2024-04-30T08:23:52.627179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
24 2
 
4.5%
31 2
 
4.5%
55 2
 
4.5%
45 2
 
4.5%
161 1
 
2.3%
72 1
 
2.3%
126 1
 
2.3%
60 1
 
2.3%
148 1
 
2.3%
16 1
 
2.3%
Other values (30) 30
68.2%
ValueCountFrequency (%)
14 1
2.3%
16 1
2.3%
18 1
2.3%
21 1
2.3%
23 1
2.3%
24 2
4.5%
28 1
2.3%
29 1
2.3%
31 2
4.5%
40 1
2.3%
ValueCountFrequency (%)
333 1
2.3%
199 1
2.3%
187 1
2.3%
162 1
2.3%
161 1
2.3%
159 1
2.3%
151 1
2.3%
149 1
2.3%
148 1
2.3%
146 1
2.3%

Interactions

2024-04-30T08:23:48.483620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:47.534796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:47.876311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.169608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.573807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:47.648799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:47.948053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.248122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.648387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:47.717113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.016327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.330530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.717136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:47.793875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.085986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T08:23:48.408643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T08:23:52.711265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시군구읍면동가구수(합계)수급권자수(합계)가구수(1종)수급권자수(1종)가구수(2종)수급권자수(2종)
번호1.0000.9321.0001.0000.8470.5480.9150.4760.051
시군구0.9321.0001.0001.0000.8170.0000.7580.1160.000
읍면동1.0001.0001.0001.0001.0001.0001.0001.0001.000
가구수(합계)1.0001.0001.0001.0001.0001.0001.0001.0001.000
수급권자수(합계)0.8470.8171.0001.0001.0001.0000.9861.0001.000
가구수(1종)0.5480.0001.0001.0001.0001.0000.9790.8090.780
수급권자수(1종)0.9150.7581.0001.0000.9860.9791.0001.0001.000
가구수(2종)0.4760.1161.0001.0001.0000.8091.0001.0000.973
수급권자수(2종)0.0510.0001.0001.0001.0000.7801.0000.9731.000
2024-04-30T08:23:52.824171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호가구수(1종)가구수(2종)수급권자수(2종)시군구
번호1.000-0.358-0.350-0.3270.828
가구수(1종)-0.3581.0000.9040.8890.000
가구수(2종)-0.3500.9041.0000.9840.046
수급권자수(2종)-0.3270.8890.9841.0000.000
시군구0.8280.0000.0460.0001.000

Missing values

2024-04-30T08:23:48.833629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T08:23:48.963247image/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

번호시도시군구읍면동가구수(합계)수급권자수(합계)가구수(1종)수급권자수(1종)가구수(2종)수급권자수(2종)
01고양시덕양구주교동522599409438113161
12고양시덕양구원신동537640431481106159
23고양시덕양구흥도동53463146452770104
34고양시덕양구성사1동59967650054299134
45고양시덕양구성사2동1261411051172124
56고양시덕양구효자동72579966169764102
67고양시덕양구삼송1동1191331081121121
78고양시덕양구삼송2동8361,001710802126199
89고양시덕양구창릉동1551761311362440
910고양시덕양구고양동81894472479394151
번호시도시군구읍면동가구수(합계)수급권자수(합계)가구수(1종)수급권자수(1종)가구수(2종)수급권자수(2종)
3435고양시일산서구일산2동46253937640986130
3536고양시일산서구일산3동668951601529
3637고양시일산서구탄현1동2542762132254151
3738고양시일산서구탄현2동2402602202292031
3839고양시일산서구주엽1동109128901001928
3940고양시일산서구주엽2동80195370380498149
4041고양시일산서구대화동37946228832391139
4142고양시일산서구송포동738760691318
4243고양시일산서구덕이동1942151611703345
4344고양시일산서구가좌동10512888971731