Overview

Dataset statistics

Number of variables9
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory84.6 B

Variable types

Text1
Numeric8

Dataset

Description화성시 기초생활수급자 현황에 대한 데이터로 구분, 2019년 기초생활보장수급권자 가구수, 2019년 기초생활보장수급권자 수, 2020년 기초생활보장수급권자 가구수, 2020년 기초생활보장수급권자 수, 2021년 기초생활보장수급권자 가구수, 2021년 기초생활보장수급권자 수, 2022년 기초생활보장수급권자 가구수, 2022년 기초생활보장수급권자 수에 대한 데이터를 포함하고 있습니다.
Author경기도 화성시
URLhttps://www.data.go.kr/data/15113471/fileData.do

Alerts

2019년 기초생활보장수급권자 가구수 is highly overall correlated with 2019년 기초생활보장수급권자수 and 6 other fieldsHigh correlation
2019년 기초생활보장수급권자수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
2020년 기초생활보장수급권자 가구수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
2020년 기초생활보장수급권자수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
2021년 기초생활보장수급권자 가구수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
2021년 기초생활보장수급권자 수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
2022년 기초생활보장수급권자 가구수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
2022년 기초생활보장수급권자 수 is highly overall correlated with 2019년 기초생활보장수급권자 가구수 and 6 other fieldsHigh correlation
구분 has unique valuesUnique
2019년 기초생활보장수급권자 가구수 has unique valuesUnique
2020년 기초생활보장수급권자 가구수 has unique valuesUnique
2021년 기초생활보장수급권자 가구수 has unique valuesUnique
2022년 기초생활보장수급권자 가구수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:43:47.794185
Analysis finished2023-12-12 19:43:56.183351
Duration8.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T04:43:56.316483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3448276
Min length3

Characters and Unicode

Total characters97
Distinct characters43
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

Unique29 ?
Unique (%)100.0%

Sample

1st row화성시
2nd row봉담읍
3rd row우정읍
4th row향남읍
5th row남양읍
ValueCountFrequency (%)
화성시 1
 
3.4%
진안동 1
 
3.4%
동탄7동 1
 
3.4%
동탄6동 1
 
3.4%
동탄5동 1
 
3.4%
동탄4동 1
 
3.4%
동탄3동 1
 
3.4%
동탄2동 1
 
3.4%
동탄1동 1
 
3.4%
화산동 1
 
3.4%
Other values (19) 19
65.5%
2023-12-13T04:43:56.672024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
23.7%
탄 9
 
9.3%
면 9
 
9.3%
읍 4
 
4.1%
남 3
 
3.1%
송 2
 
2.1%
2 2
 
2.1%
1 2
 
2.1%
점 2
 
2.1%
병 2
 
2.1%
Other values (33) 39
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
89.7%
Decimal Number 10
 
10.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
26.4%
탄 9
 
10.3%
면 9
 
10.3%
읍 4
 
4.6%
남 3
 
3.4%
송 2
 
2.3%
점 2
 
2.3%
병 2
 
2.3%
안 2
 
2.3%
산 2
 
2.3%
Other values (25) 29
33.3%
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 87
89.7%
Common 10
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
26.4%
탄 9
 
10.3%
면 9
 
10.3%
읍 4
 
4.6%
남 3
 
3.4%
송 2
 
2.3%
점 2
 
2.3%
병 2
 
2.3%
안 2
 
2.3%
산 2
 
2.3%
Other values (25) 29
33.3%
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 87
89.7%
ASCII 10
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
26.4%
탄 9
 
10.3%
면 9
 
10.3%
읍 4
 
4.6%
남 3
 
3.4%
송 2
 
2.3%
점 2
 
2.3%
병 2
 
2.3%
안 2
 
2.3%
산 2
 
2.3%
Other values (25) 29
33.3%
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%

2019년 기초생활보장수급권자 가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.2069
Minimum31
Maximum1168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:56.837625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile45.4
Q1104
median193
Q3317
95-th percentile949.2
Maximum1168
Range1137
Interquartile range (IQR)213

Descriptive statistics

Standard deviation289.33562
Coefficient of variation (CV)1.0362768
Kurtosis4.7552037
Mean279.2069
Median Absolute Deviation (MAD)112
Skewness2.1661573
Sum8097
Variance83715.099
MonotonicityNot monotonic
2023-12-13T04:43:56.962423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
500 1
 
3.4%
1168 1
 
3.4%
39 1
 
3.4%
138 1
 
3.4%
69 1
 
3.4%
624 1
 
3.4%
55 1
 
3.4%
284 1
 
3.4%
275 1
 
3.4%
56 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
31 1
3.4%
39 1
3.4%
55 1
3.4%
56 1
3.4%
69 1
3.4%
78 1
3.4%
81 1
3.4%
104 1
3.4%
115 1
3.4%
121 1
3.4%
ValueCountFrequency (%)
1168 1
3.4%
1166 1
3.4%
624 1
3.4%
500 1
3.4%
497 1
3.4%
470 1
3.4%
328 1
3.4%
317 1
3.4%
284 1
3.4%
275 1
3.4%

2019년 기초생활보장수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.41379
Minimum43
Maximum1734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:57.094001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile65.2
Q1143
median250
Q3488
95-th percentile1261
Maximum1734
Range1691
Interquartile range (IQR)345

Descriptive statistics

Standard deviation406.62509
Coefficient of variation (CV)1.0633118
Kurtosis5.7263213
Mean382.41379
Median Absolute Deviation (MAD)157
Skewness2.3313233
Sum11090
Variance165343.97
MonotonicityNot monotonic
2023-12-13T04:43:57.235916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
252 2
 
6.9%
499 1
 
3.4%
216 1
 
3.4%
58 1
 
3.4%
224 1
 
3.4%
100 1
 
3.4%
760 1
 
3.4%
78 1
 
3.4%
488 1
 
3.4%
462 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
43 1
3.4%
58 1
3.4%
76 1
3.4%
78 1
3.4%
93 1
3.4%
100 1
3.4%
108 1
3.4%
143 1
3.4%
151 1
3.4%
153 1
3.4%
ValueCountFrequency (%)
1734 1
3.4%
1595 1
3.4%
760 1
3.4%
669 1
3.4%
641 1
3.4%
517 1
3.4%
499 1
3.4%
488 1
3.4%
462 1
3.4%
411 1
3.4%

2020년 기초생활보장수급권자 가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.06897
Minimum56
Maximum1395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:57.362405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile69.4
Q1109
median215
Q3369
95-th percentile1084.8
Maximum1395
Range1339
Interquartile range (IQR)260

Descriptive statistics

Standard deviation334.09087
Coefficient of variation (CV)1.0091277
Kurtosis4.8341856
Mean331.06897
Median Absolute Deviation (MAD)120
Skewness2.1834513
Sum9601
Variance111616.71
MonotonicityNot monotonic
2023-12-13T04:43:57.485091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
473 1
 
3.4%
1395 1
 
3.4%
89 1
 
3.4%
400 1
 
3.4%
109 1
 
3.4%
735 1
 
3.4%
59 1
 
3.4%
357 1
 
3.4%
335 1
 
3.4%
96 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
56 1
3.4%
59 1
3.4%
85 1
3.4%
89 1
3.4%
96 1
3.4%
99 1
3.4%
108 1
3.4%
109 1
3.4%
125 1
3.4%
131 1
3.4%
ValueCountFrequency (%)
1395 1
3.4%
1318 1
3.4%
735 1
3.4%
632 1
3.4%
566 1
3.4%
473 1
3.4%
400 1
3.4%
369 1
3.4%
357 1
3.4%
341 1
3.4%

2020년 기초생활보장수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean464.17241
Minimum92
Maximum1969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:57.596572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile100.4
Q1160
median274
Q3608
95-th percentile1533
Maximum1969
Range1877
Interquartile range (IQR)448

Descriptive statistics

Standard deviation476.95036
Coefficient of variation (CV)1.0275285
Kurtosis5.3501061
Mean464.17241
Median Absolute Deviation (MAD)158
Skewness2.2765543
Sum13461
Variance227481.65
MonotonicityNot monotonic
2023-12-13T04:43:57.725675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
608 2
 
6.9%
472 1
 
3.4%
754 1
 
3.4%
153 1
 
3.4%
637 1
 
3.4%
185 1
 
3.4%
912 1
 
3.4%
110 1
 
3.4%
584 1
 
3.4%
143 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
92 1
3.4%
94 1
3.4%
110 1
3.4%
142 1
3.4%
143 1
3.4%
153 1
3.4%
157 1
3.4%
160 1
3.4%
165 1
3.4%
168 1
3.4%
ValueCountFrequency (%)
1969 1
3.4%
1947 1
3.4%
912 1
3.4%
868 1
3.4%
754 1
3.4%
637 1
3.4%
608 2
6.9%
584 1
3.4%
472 1
3.4%
437 1
3.4%

2021년 기초생활보장수급권자 가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.72414
Minimum57
Maximum1643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:57.850364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile72.6
Q1131
median245
Q3421
95-th percentile1259.4
Maximum1643
Range1586
Interquartile range (IQR)290

Descriptive statistics

Standard deviation387.72191
Coefficient of variation (CV)1.0104183
Kurtosis4.650194
Mean383.72414
Median Absolute Deviation (MAD)138
Skewness2.1566029
Sum11128
Variance150328.28
MonotonicityNot monotonic
2023-12-13T04:43:57.974286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
498 1
 
3.4%
1643 1
 
3.4%
121 1
 
3.4%
513 1
 
3.4%
131 1
 
3.4%
945 1
 
3.4%
65 1
 
3.4%
421 1
 
3.4%
387 1
 
3.4%
107 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
57 1
3.4%
65 1
3.4%
84 1
3.4%
99 1
3.4%
107 1
3.4%
110 1
3.4%
121 1
3.4%
131 1
3.4%
162 1
3.4%
169 1
3.4%
ValueCountFrequency (%)
1643 1
3.4%
1469 1
3.4%
945 1
3.4%
724 1
3.4%
650 1
3.4%
513 1
3.4%
498 1
3.4%
421 1
3.4%
402 1
3.4%
387 1
3.4%

2021년 기초생활보장수급권자 수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean532.7931
Minimum93
Maximum2272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:58.094015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile100
Q1208
median309
Q3650
95-th percentile1772.2
Maximum2272
Range2179
Interquartile range (IQR)442

Descriptive statistics

Standard deviation546.3326
Coefficient of variation (CV)1.0254123
Kurtosis4.8416497
Mean532.7931
Median Absolute Deviation (MAD)160
Skewness2.1939617
Sum15451
Variance298479.31
MonotonicityNot monotonic
2023-12-13T04:43:58.199247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
100 2
 
6.9%
498 1
 
3.4%
859 1
 
3.4%
227 1
 
3.4%
753 1
 
3.4%
211 1
 
3.4%
1207 1
 
3.4%
745 1
 
3.4%
650 1
 
3.4%
149 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
93 1
3.4%
100 2
6.9%
145 1
3.4%
149 1
3.4%
164 1
3.4%
199 1
3.4%
208 1
3.4%
211 1
3.4%
227 1
3.4%
267 1
3.4%
ValueCountFrequency (%)
2272 1
3.4%
2149 1
3.4%
1207 1
3.4%
1023 1
3.4%
859 1
3.4%
753 1
3.4%
745 1
3.4%
650 1
3.4%
629 1
3.4%
498 1
3.4%

2022년 기초생활보장수급권자 가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean415.48276
Minimum52
Maximum1712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:58.307706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile66
Q1143
median263
Q3470
95-th percentile1351
Maximum1712
Range1660
Interquartile range (IQR)327

Descriptive statistics

Standard deviation415.77309
Coefficient of variation (CV)1.0006988
Kurtosis3.5382129
Mean415.48276
Median Absolute Deviation (MAD)149
Skewness1.9479404
Sum12049
Variance172867.26
MonotonicityNot monotonic
2023-12-13T04:43:58.425692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
478 1
 
3.4%
1712 1
 
3.4%
120 1
 
3.4%
600 1
 
3.4%
143 1
 
3.4%
1117 1
 
3.4%
52 1
 
3.4%
470 1
 
3.4%
412 1
 
3.4%
97 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
52 1
3.4%
54 1
3.4%
84 1
3.4%
97 1
3.4%
110 1
3.4%
114 1
3.4%
120 1
3.4%
143 1
3.4%
167 1
3.4%
195 1
3.4%
ValueCountFrequency (%)
1712 1
3.4%
1507 1
3.4%
1117 1
3.4%
905 1
3.4%
701 1
3.4%
600 1
3.4%
478 1
3.4%
470 1
3.4%
420 1
3.4%
412 1
3.4%

2022년 기초생활보장수급권자 수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean573.58621
Minimum83
Maximum2333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T04:43:58.543404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile93.2
Q1212
median331
Q3682
95-th percentile1872.2
Maximum2333
Range2250
Interquartile range (IQR)470

Descriptive statistics

Standard deviation577.04385
Coefficient of variation (CV)1.0060281
Kurtosis3.629149
Mean573.58621
Median Absolute Deviation (MAD)183
Skewness1.9544165
Sum16634
Variance332979.61
MonotonicityNot monotonic
2023-12-13T04:43:58.649554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
313 2
 
6.9%
478 1
 
3.4%
364 1
 
3.4%
215 1
 
3.4%
888 1
 
3.4%
212 1
 
3.4%
1391 1
 
3.4%
83 1
 
3.4%
842 1
 
3.4%
682 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
83 1
3.4%
90 1
3.4%
98 1
3.4%
147 1
3.4%
162 1
3.4%
179 1
3.4%
210 1
3.4%
212 1
3.4%
215 1
3.4%
246 1
3.4%
ValueCountFrequency (%)
2333 1
3.4%
2193 1
3.4%
1391 1
3.4%
1243 1
3.4%
928 1
3.4%
888 1
3.4%
842 1
3.4%
682 1
3.4%
664 1
3.4%
567 1
3.4%

Interactions

2023-12-13T04:43:55.103949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.117054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.018347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.966900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.934368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.900314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.859084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.168183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.201794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.229429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.134830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.091604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.063526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.035854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.991880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.271264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.305716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.346731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.251752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.219840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.186163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.152086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:53.110035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.393403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.403699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.454050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.382396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.327988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.332124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.287396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:53.254793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.519211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.515967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.580443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.511074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.472087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.448203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.421870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:53.372216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.659261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.625814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.716370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.631011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.604096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.578499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.532090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:53.499461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.768419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.713338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.814998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.738542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.711170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.665681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.634029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:53.934973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.878151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.818820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:48.926604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:49.862640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:50.823704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:51.782315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:52.745997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:54.057881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:43:55.010324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:43:58.743453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2019년 기초생활보장수급권자 가구수2019년 기초생활보장수급권자수2020년 기초생활보장수급권자 가구수2020년 기초생활보장수급권자수2021년 기초생활보장수급권자 가구수2021년 기초생활보장수급권자 수2022년 기초생활보장수급권자 가구수2022년 기초생활보장수급권자 수
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
2019년 기초생활보장수급권자 가구수1.0001.0000.9610.9840.8910.9210.9730.9370.849
2019년 기초생활보장수급권자수1.0000.9611.0000.9180.9650.9370.9060.9740.952
2020년 기초생활보장수급권자 가구수1.0000.9840.9181.0000.9090.9430.9860.9170.842
2020년 기초생활보장수급권자수1.0000.8910.9650.9091.0000.9670.9360.8870.972
2021년 기초생활보장수급권자 가구수1.0000.9210.9370.9430.9671.0000.9280.9950.872
2021년 기초생활보장수급권자 수1.0000.9730.9060.9860.9360.9281.0000.9450.907
2022년 기초생활보장수급권자 가구수1.0000.9370.9740.9170.8870.9950.9451.0000.919
2022년 기초생활보장수급권자 수1.0000.8490.9520.8420.9720.8720.9070.9191.000
2023-12-13T04:43:58.872293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019년 기초생활보장수급권자 가구수2019년 기초생활보장수급권자수2020년 기초생활보장수급권자 가구수2020년 기초생활보장수급권자수2021년 기초생활보장수급권자 가구수2021년 기초생활보장수급권자 수2022년 기초생활보장수급권자 가구수2022년 기초생활보장수급권자 수
2019년 기초생활보장수급권자 가구수1.0000.9890.9560.9250.9340.9060.9180.895
2019년 기초생활보장수급권자수0.9891.0000.9690.9530.9490.9300.9340.919
2020년 기초생활보장수급권자 가구수0.9560.9691.0000.9870.9910.9700.9760.958
2020년 기초생활보장수급권자수0.9250.9530.9871.0000.9830.9830.9740.970
2021년 기초생활보장수급권자 가구수0.9340.9490.9910.9831.0000.9870.9850.975
2021년 기초생활보장수급권자 수0.9060.9300.9700.9830.9871.0000.9850.990
2022년 기초생활보장수급권자 가구수0.9180.9340.9760.9740.9850.9851.0000.990
2022년 기초생활보장수급권자 수0.8950.9190.9580.9700.9750.9900.9901.000

Missing values

2023-12-13T04:43:55.951873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:43:56.126033image/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

구분2019년 기초생활보장수급권자 가구수2019년 기초생활보장수급권자수2020년 기초생활보장수급권자 가구수2020년 기초생활보장수급권자수2021년 기초생활보장수급권자 가구수2021년 기초생활보장수급권자 수2022년 기초생활보장수급권자 가구수2022년 기초생활보장수급권자 수
0화성시500499473472498498478478
1봉담읍11681595139519471643227217122333
2우정읍317411341437363463391514
3향남읍11661734131819691469214915072193
4남양읍49766963286872410239051243
5매송면242344269374298413305418
6비봉면115143125160217275263331
7마도면134165140168169199167210
8송산면197252232300245313252313
9서신면176214189222228267220259
구분2019년 기초생활보장수급권자 가구수2019년 기초생활보장수급권자수2020년 기초생활보장수급권자 가구수2020년 기초생활보장수급권자수2021년 기초생활보장수급권자 가구수2021년 기초생활보장수급권자 수2022년 기초생활보장수급권자 가구수2022년 기초생활보장수급권자 수
19기배동10415110815799145110162
20화산동261394290432328468409567
21동탄1동56769614310714997147
22동탄2동275462335584387650412682
23동탄3동284488357608421745470842
24동탄4동557859110651005283
25동탄5동624760735912945120711171391
26동탄6동69100109185131211143212
27동탄7동138224400637513753600888
28동탄8동395889153121227120215