Overview

Dataset statistics

Number of variables12
Number of observations76
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory108.7 B

Variable types

Categorical2
Numeric10

Dataset

Description경기도 평택시의 행정동별 기초생활수급자현황(수급자가구수, 수급자인원, 생계급여, 의료급여, 주거급여, 교육급여 등)을 제공합니다.※ 문의 : 평택시청 사회복지과(031-8024-3031)
Author경기도 평택시
URLhttps://www.data.go.kr/data/15113467/fileData.do

Alerts

총수급자 가구(중복제외) is highly overall correlated with 총수급자 인원(중복제외) and 9 other fieldsHigh correlation
총수급자 인원(중복제외) is highly overall correlated with 총수급자 가구(중복제외) and 9 other fieldsHigh correlation
생계급여 가구 is highly overall correlated with 총수급자 가구(중복제외) and 9 other fieldsHigh correlation
생계급여 인원 is highly overall correlated with 총수급자 가구(중복제외) and 9 other fieldsHigh correlation
의료급여 가구 is highly overall correlated with 총수급자 가구(중복제외) and 8 other fieldsHigh correlation
의료급여 인원 is highly overall correlated with 총수급자 가구(중복제외) and 8 other fieldsHigh correlation
주거급여 가구 is highly overall correlated with 총수급자 가구(중복제외) and 9 other fieldsHigh correlation
주거급여 인원 is highly overall correlated with 총수급자 가구(중복제외) and 8 other fieldsHigh correlation
교육급여 가구 is highly overall correlated with 총수급자 가구(중복제외) and 8 other fieldsHigh correlation
교육급여 인원 is highly overall correlated with 총수급자 가구(중복제외) and 8 other fieldsHigh correlation
구분 is highly overall correlated with 총수급자 가구(중복제외) and 4 other fieldsHigh correlation

Reproduction

Analysis started2024-03-14 15:15:25.444363
Analysis finished2024-03-14 15:15:44.592998
Duration19.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size736.0 B
2023
26 
2021
25 
2022
25 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2023 26
34.2%
2021 25
32.9%
2022 25
32.9%

Length

2024-03-15T00:15:44.793567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:15:45.122657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 26
34.2%
2021 25
32.9%
2022 25
32.9%

구분
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size736.0 B
팽성읍
 
3
안중읍
 
3
포승읍
 
3
청북읍
 
3
진위면
 
3
Other values (21)
61 

Length

Max length4
Median length3
Mean length3.1710526
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row팽성읍
2nd row안중읍
3rd row포승읍
4th row청북읍
5th row진위면

Common Values

ValueCountFrequency (%)
팽성읍 3
 
3.9%
안중읍 3
 
3.9%
포승읍 3
 
3.9%
청북읍 3
 
3.9%
진위면 3
 
3.9%
서탄면 3
 
3.9%
고덕면 3
 
3.9%
오성면 3
 
3.9%
현덕면 3
 
3.9%
중앙동 3
 
3.9%
Other values (16) 46
60.5%

Length

2024-03-15T00:15:45.491354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
팽성읍 3
 
3.9%
안중읍 3
 
3.9%
고덕동 3
 
3.9%
동삭동 3
 
3.9%
용이동 3
 
3.9%
세교동 3
 
3.9%
비전2동 3
 
3.9%
비전1동 3
 
3.9%
통복동 3
 
3.9%
원평동 3
 
3.9%
Other values (16) 46
60.5%

총수급자 가구(중복제외)
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.82895
Minimum70
Maximum1598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:45.867913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile102
Q1231.5
median344
Q3643.75
95-th percentile1251.25
Maximum1598
Range1528
Interquartile range (IQR)412.25

Descriptive statistics

Standard deviation390.46196
Coefficient of variation (CV)0.75843047
Kurtosis0.0071229006
Mean514.82895
Median Absolute Deviation (MAD)143
Skewness1.1008818
Sum39127
Variance152460.54
MonotonicityNot monotonic
2024-03-15T00:15:46.317606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
349 2
 
2.6%
238 2
 
2.6%
70 2
 
2.6%
573 2
 
2.6%
1263 1
 
1.3%
189 1
 
1.3%
80 1
 
1.3%
314 1
 
1.3%
430 1
 
1.3%
937 1
 
1.3%
Other values (62) 62
81.6%
ValueCountFrequency (%)
70 2
2.6%
80 1
1.3%
93 1
1.3%
105 1
1.3%
109 1
1.3%
174 1
1.3%
179 1
1.3%
184 1
1.3%
189 1
1.3%
202 1
1.3%
ValueCountFrequency (%)
1598 1
1.3%
1443 1
1.3%
1263 1
1.3%
1258 1
1.3%
1249 1
1.3%
1247 1
1.3%
1234 1
1.3%
1211 1
1.3%
1195 1
1.3%
1140 1
1.3%

총수급자 인원(중복제외)
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean778.36842
Minimum99
Maximum2340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:46.749130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile145.5
Q1387.75
median527.5
Q31132
95-th percentile1906
Maximum2340
Range2241
Interquartile range (IQR)744.25

Descriptive statistics

Standard deviation587.29102
Coefficient of variation (CV)0.75451548
Kurtosis-0.044473714
Mean778.36842
Median Absolute Deviation (MAD)214
Skewness1.0802602
Sum59156
Variance344910.74
MonotonicityNot monotonic
2024-03-15T00:15:47.367110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
490 2
 
2.6%
304 2
 
2.6%
462 1
 
1.3%
425 1
 
1.3%
369 1
 
1.3%
644 1
 
1.3%
1450 1
 
1.3%
1757 1
 
1.3%
580 1
 
1.3%
469 1
 
1.3%
Other values (64) 64
84.2%
ValueCountFrequency (%)
99 1
1.3%
104 1
1.3%
124 1
1.3%
129 1
1.3%
151 1
1.3%
152 1
1.3%
209 1
1.3%
214 1
1.3%
232 1
1.3%
273 1
1.3%
ValueCountFrequency (%)
2340 1
1.3%
2118 1
1.3%
2040 1
1.3%
1939 1
1.3%
1895 1
1.3%
1859 1
1.3%
1812 1
1.3%
1796 1
1.3%
1795 1
1.3%
1785 1
1.3%

생계급여 가구
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.68421
Minimum47
Maximum943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:47.601346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile67
Q1140.75
median233.5
Q3438
95-th percentile869.5
Maximum943
Range896
Interquartile range (IQR)297.25

Descriptive statistics

Standard deviation254.57453
Coefficient of variation (CV)0.76984181
Kurtosis-0.19820114
Mean330.68421
Median Absolute Deviation (MAD)108.5
Skewness1.0489503
Sum25132
Variance64808.192
MonotonicityNot monotonic
2024-03-15T00:15:47.962325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251 3
 
3.9%
234 2
 
2.6%
141 2
 
2.6%
249 2
 
2.6%
183 2
 
2.6%
867 1
 
1.3%
51 1
 
1.3%
217 1
 
1.3%
257 1
 
1.3%
580 1
 
1.3%
Other values (60) 60
78.9%
ValueCountFrequency (%)
47 1
1.3%
49 1
1.3%
51 1
1.3%
64 1
1.3%
68 1
1.3%
74 1
1.3%
107 1
1.3%
111 1
1.3%
112 1
1.3%
117 1
1.3%
ValueCountFrequency (%)
943 1
1.3%
907 1
1.3%
895 1
1.3%
877 1
1.3%
867 1
1.3%
787 1
1.3%
786 1
1.3%
771 1
1.3%
728 1
1.3%
719 1
1.3%

생계급여 인원
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.89474
Minimum57
Maximum1193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:48.360397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile80.5
Q1190.25
median284.5
Q3536.25
95-th percentile1122.5
Maximum1193
Range1136
Interquartile range (IQR)346

Descriptive statistics

Standard deviation326.97949
Coefficient of variation (CV)0.76955411
Kurtosis-0.13702557
Mean424.89474
Median Absolute Deviation (MAD)122
Skewness1.0717144
Sum32292
Variance106915.59
MonotonicityNot monotonic
2024-03-15T00:15:48.793230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176 2
 
2.6%
251 2
 
2.6%
57 2
 
2.6%
208 2
 
2.6%
175 2
 
2.6%
206 2
 
2.6%
127 2
 
2.6%
735 1
 
1.3%
167 1
 
1.3%
171 1
 
1.3%
Other values (59) 59
77.6%
ValueCountFrequency (%)
57 2
2.6%
61 1
1.3%
79 1
1.3%
81 1
1.3%
90 1
1.3%
127 2
2.6%
136 1
1.3%
148 1
1.3%
167 1
1.3%
169 1
1.3%
ValueCountFrequency (%)
1193 1
1.3%
1165 1
1.3%
1138 1
1.3%
1136 1
1.3%
1118 1
1.3%
1072 1
1.3%
1034 1
1.3%
1028 1
1.3%
919 1
1.3%
913 1
1.3%

의료급여 가구
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.684211
Minimum2
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:49.206049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q111
median20
Q336
95-th percentile76.25
Maximum101
Range99
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.154067
Coefficient of variation (CV)0.84206839
Kurtosis0.697111
Mean28.684211
Median Absolute Deviation (MAD)10
Skewness1.2992271
Sum2180
Variance583.41895
MonotonicityNot monotonic
2024-03-15T00:15:49.641099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
11 5
 
6.6%
21 4
 
5.3%
16 4
 
5.3%
10 4
 
5.3%
35 3
 
3.9%
12 3
 
3.9%
9 3
 
3.9%
5 3
 
3.9%
17 2
 
2.6%
22 2
 
2.6%
Other values (33) 43
56.6%
ValueCountFrequency (%)
2 1
 
1.3%
3 1
 
1.3%
4 1
 
1.3%
5 3
3.9%
8 2
 
2.6%
9 3
3.9%
10 4
5.3%
11 5
6.6%
12 3
3.9%
13 2
 
2.6%
ValueCountFrequency (%)
101 1
1.3%
94 1
1.3%
78 1
1.3%
77 1
1.3%
76 1
1.3%
75 2
2.6%
74 1
1.3%
72 1
1.3%
64 1
1.3%
63 1
1.3%

의료급여 인원
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.328947
Minimum2
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:50.046713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.75
Q117.75
median29.5
Q362.5
95-th percentile131.75
Maximum184
Range182
Interquartile range (IQR)44.75

Descriptive statistics

Standard deviation41.227624
Coefficient of variation (CV)0.90952089
Kurtosis1.3478239
Mean45.328947
Median Absolute Deviation (MAD)13.5
Skewness1.4710475
Sum3445
Variance1699.717
MonotonicityNot monotonic
2024-03-15T00:15:50.485253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 4
 
5.3%
24 4
 
5.3%
30 4
 
5.3%
14 3
 
3.9%
21 3
 
3.9%
23 3
 
3.9%
10 2
 
2.6%
22 2
 
2.6%
36 2
 
2.6%
99 2
 
2.6%
Other values (41) 47
61.8%
ValueCountFrequency (%)
2 1
 
1.3%
4 1
 
1.3%
6 1
 
1.3%
7 1
 
1.3%
8 1
 
1.3%
9 1
 
1.3%
10 2
2.6%
11 2
2.6%
14 3
3.9%
16 4
5.3%
ValueCountFrequency (%)
184 1
1.3%
149 1
1.3%
143 1
1.3%
137 1
1.3%
130 1
1.3%
125 1
1.3%
122 1
1.3%
119 1
1.3%
114 1
1.3%
99 2
2.6%

주거급여 가구
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.61842
Minimum3
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:50.923350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21
Q156
median87
Q3167.25
95-th percentile323.5
Maximum516
Range513
Interquartile range (IQR)111.25

Descriptive statistics

Standard deviation106.63082
Coefficient of variation (CV)0.82904783
Kurtosis2.209911
Mean128.61842
Median Absolute Deviation (MAD)39.5
Skewness1.5303977
Sum9775
Variance11370.132
MonotonicityNot monotonic
2024-03-15T00:15:51.197968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 3
 
3.9%
56 2
 
2.6%
21 2
 
2.6%
42 2
 
2.6%
78 2
 
2.6%
115 2
 
2.6%
105 2
 
2.6%
66 2
 
2.6%
51 2
 
2.6%
100 1
 
1.3%
Other values (56) 56
73.7%
ValueCountFrequency (%)
3 1
1.3%
12 1
1.3%
15 1
1.3%
21 2
2.6%
24 1
1.3%
30 1
1.3%
36 1
1.3%
39 1
1.3%
42 2
2.6%
43 1
1.3%
ValueCountFrequency (%)
516 1
1.3%
447 1
1.3%
403 1
1.3%
346 1
1.3%
316 1
1.3%
309 1
1.3%
284 1
1.3%
280 1
1.3%
273 1
1.3%
263 1
1.3%

주거급여 인원
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.36842
Minimum3
Maximum865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:51.451382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile37
Q197.5
median164.5
Q3285.75
95-th percentile578.25
Maximum865
Range862
Interquartile range (IQR)188.25

Descriptive statistics

Standard deviation184.05505
Coefficient of variation (CV)0.82399764
Kurtosis1.4433677
Mean223.36842
Median Absolute Deviation (MAD)84.5
Skewness1.3500365
Sum16976
Variance33876.262
MonotonicityNot monotonic
2024-03-15T00:15:51.690053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 3
 
3.9%
116 2
 
2.6%
407 1
 
1.3%
3 1
 
1.3%
47 1
 
1.3%
91 1
 
1.3%
130 1
 
1.3%
199 1
 
1.3%
523 1
 
1.3%
402 1
 
1.3%
Other values (63) 63
82.9%
ValueCountFrequency (%)
3 1
1.3%
25 1
1.3%
28 1
1.3%
31 1
1.3%
39 1
1.3%
43 1
1.3%
46 1
1.3%
47 1
1.3%
53 1
1.3%
55 1
1.3%
ValueCountFrequency (%)
865 1
1.3%
722 1
1.3%
624 1
1.3%
621 1
1.3%
564 1
1.3%
555 1
1.3%
531 1
1.3%
525 1
1.3%
523 1
1.3%
467 1
1.3%

교육급여 가구
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.842105
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:51.930020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111.75
median22.5
Q338.5
95-th percentile64.5
Maximum96
Range95
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation19.933257
Coefficient of variation (CV)0.74261154
Kurtosis1.0612033
Mean26.842105
Median Absolute Deviation (MAD)12.5
Skewness1.1169625
Sum2040
Variance397.33474
MonotonicityNot monotonic
2024-03-15T00:15:52.276370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
15 4
 
5.3%
17 4
 
5.3%
14 3
 
3.9%
23 3
 
3.9%
4 3
 
3.9%
9 3
 
3.9%
10 3
 
3.9%
6 3
 
3.9%
37 2
 
2.6%
47 2
 
2.6%
Other values (35) 46
60.5%
ValueCountFrequency (%)
1 1
 
1.3%
3 1
 
1.3%
4 3
3.9%
5 2
2.6%
6 3
3.9%
8 1
 
1.3%
9 3
3.9%
10 3
3.9%
11 2
2.6%
12 1
 
1.3%
ValueCountFrequency (%)
96 1
1.3%
77 1
1.3%
69 1
1.3%
66 1
1.3%
64 2
2.6%
60 1
1.3%
56 1
1.3%
50 2
2.6%
49 1
1.3%
47 2
2.6%

교육급여 인원
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.776316
Minimum2
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size812.0 B
2024-03-15T00:15:52.521020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11.5
Q136
median69.5
Q3122.75
95-th percentile210.75
Maximum317
Range315
Interquartile range (IQR)86.75

Descriptive statistics

Standard deviation63.978246
Coefficient of variation (CV)0.75467122
Kurtosis1.6021505
Mean84.776316
Median Absolute Deviation (MAD)40
Skewness1.2269599
Sum6443
Variance4093.216
MonotonicityNot monotonic
2024-03-15T00:15:52.786457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 3
 
3.9%
33 2
 
2.6%
75 2
 
2.6%
139 2
 
2.6%
20 2
 
2.6%
22 2
 
2.6%
37 2
 
2.6%
27 2
 
2.6%
129 2
 
2.6%
42 2
 
2.6%
Other values (54) 55
72.4%
ValueCountFrequency (%)
2 1
1.3%
6 1
1.3%
8 1
1.3%
10 1
1.3%
12 1
1.3%
15 1
1.3%
18 1
1.3%
20 2
2.6%
22 2
2.6%
27 2
2.6%
ValueCountFrequency (%)
317 1
1.3%
255 1
1.3%
214 1
1.3%
213 1
1.3%
210 1
1.3%
209 1
1.3%
185 1
1.3%
176 1
1.3%
163 1
1.3%
161 1
1.3%

Interactions

2024-03-15T00:15:41.267874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:26.186628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:28.443347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.943947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.419879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.939215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.531795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:36.297020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:37.893969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.379616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:41.537065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:26.456887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:28.602005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:30.096281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.580087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:33.187936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.701139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:36.458109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.071025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.538880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:41.773443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:26.705296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:28.730170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:30.227282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.714726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:33.326159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.841235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:36.621130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.201093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.674707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:42.005928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:26.943519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:28.862982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:30.388047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.910813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:33.451526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.977321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:36.804169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.326195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.800924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:42.249008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:27.390643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.013849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:30.522249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.069430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:33.593806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:35.125090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:36.942834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.460095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.943695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:42.494184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:27.547108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.157237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:30.714890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.208813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:33.729487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:35.274685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:37.095636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.598199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:40.083929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:42.754503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:27.728813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.306340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:30.886729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.362189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:33.882669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:35.489286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:37.247730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.746921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:40.323553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:42.999497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:27.881114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.486917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.022481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.501182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.022482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:35.667972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:37.388550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:38.881886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:40.559939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:43.234818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:28.033791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.660205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.147161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.634491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.155797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:35.807505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:37.519844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.072717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:40.791395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:43.479555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:28.203045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:29.797860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:31.281831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:32.791471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:34.386428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:36.144730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:37.661717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:39.235089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:15:41.025421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:15:52.971048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도구분총수급자 가구(중복제외)총수급자 인원(중복제외)생계급여 가구생계급여 인원의료급여 가구의료급여 인원주거급여 가구주거급여 인원교육급여 가구교육급여 인원
기준연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
구분0.0001.0000.9400.9790.9690.9720.8550.8420.8960.8810.8380.820
총수급자 가구(중복제외)0.0000.9401.0000.9220.8410.8910.8970.8220.9040.8910.8140.799
총수급자 인원(중복제외)0.0000.9790.9221.0000.9480.9560.7760.9120.9620.9550.8150.775
생계급여 가구0.0000.9690.8410.9481.0000.9900.7550.8970.8770.8890.7610.704
생계급여 인원0.0000.9720.8910.9560.9901.0000.7850.9100.9030.9010.7950.767
의료급여 가구0.0000.8550.8970.7760.7550.7851.0000.9120.7830.7880.8320.808
의료급여 인원0.0000.8420.8220.9120.8970.9100.9121.0000.8890.9190.7970.779
주거급여 가구0.0000.8960.9040.9620.8770.9030.7830.8891.0000.9830.7090.700
주거급여 인원0.0000.8810.8910.9550.8890.9010.7880.9190.9831.0000.8100.841
교육급여 가구0.0000.8380.8140.8150.7610.7950.8320.7970.7090.8101.0000.992
교육급여 인원0.0000.8200.7990.7750.7040.7670.8080.7790.7000.8410.9921.000
2024-03-15T00:15:53.237932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기준연도
구분1.0000.000
기준연도0.0001.000
2024-03-15T00:15:53.434370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총수급자 가구(중복제외)총수급자 인원(중복제외)생계급여 가구생계급여 인원의료급여 가구의료급여 인원주거급여 가구주거급여 인원교육급여 가구교육급여 인원기준연도구분
총수급자 가구(중복제외)1.0000.9770.9790.9850.8650.8460.9160.8830.8000.8000.0000.626
총수급자 인원(중복제외)0.9771.0000.9340.9600.8730.8660.9480.9380.8500.8470.0000.757
생계급여 가구0.9790.9341.0000.9900.8350.8080.8480.8030.7370.7350.0000.717
생계급여 인원0.9850.9600.9901.0000.8370.8140.8690.8390.7660.7620.0000.728
의료급여 가구0.8650.8730.8350.8371.0000.9720.8450.8300.7140.7150.0000.459
의료급여 인원0.8460.8660.8080.8140.9721.0000.8490.8460.7700.7700.0000.434
주거급여 가구0.9160.9480.8480.8690.8450.8491.0000.9850.8450.8480.0000.521
주거급여 인원0.8830.9380.8030.8390.8300.8460.9851.0000.8520.8530.0000.495
교육급여 가구0.8000.8500.7370.7660.7140.7700.8450.8521.0000.9950.0000.447
교육급여 인원0.8000.8470.7350.7620.7150.7700.8480.8530.9951.0000.0000.435
기준연도0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
구분0.6260.7570.7170.7280.4590.4340.5210.4950.4470.4350.0001.000

Missing values

2024-03-15T00:15:43.847549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:15:44.378332image/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

기준연도구분총수급자 가구(중복제외)총수급자 인원(중복제외)생계급여 가구생계급여 인원의료급여 가구의료급여 인원주거급여 가구주거급여 인원교육급여 가구교육급여 인원
02021팽성읍1247179686711187713025440749141
12021안중읍8451302528673427823742538126
22021포승읍37256523028816248613840115
32021청북읍2273741411851114511002475
42021진위면316448207251203066962371
52021서탄면7099495759122548
62021고덕면233368149208172850791753
72021오성면20228713917511214369922
82021현덕면931296479442131415
92021중앙동108717856368717814930955564210
기준연도구분총수급자 가구(중복제외)총수급자 인원(중복제외)생계급여 가구생계급여 인원의료급여 가구의료급여 인원주거급여 가구주거급여 인원교육급여 가구교육급여 인원
662023신장2동3284362342811011741111033
672023신평동983125471983357861802512784
682023원평동365502247300213088144928
692023통복동17920912313610104253410
702023비전1동1598234094311939414351686545139
712023비전2동6791153395533356220240947149
722023세교동36761620027710141292432882
732023용이동3264911772183641971761656
742023동삭동28956715626711181072361546
752023고덕동41762223430237451332381337