Overview

Dataset statistics

Number of variables10
Number of observations1457
Missing cells2660
Missing cells (%)18.3%
Duplicate rows56
Duplicate rows (%)3.8%
Total size in memory122.5 KiB
Average record size in memory86.1 B

Variable types

Text2
Categorical2
Numeric6

Dataset

Description2022년말 현재 부산시 기초생활보장수급자 중 65세 이상 수급자에 대한 데이터로, 수급자현황, 남녀 구분등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15113742/fileData.do

Alerts

Dataset has 56 (3.8%) duplicate rowsDuplicates
합계 is highly overall correlated with 65~69세 and 5 other fieldsHigh correlation
65~69세 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
70~74세 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
75~79세 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
80~89세 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
90세이상 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
수급자구분 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
구군명 has 1441 (98.9%) missing valuesMissing
읍면동 has 1219 (83.7%) missing valuesMissing
65~69세 has 455 (31.2%) zerosZeros
70~74세 has 569 (39.1%) zerosZeros
75~79세 has 590 (40.5%) zerosZeros
80~89세 has 435 (29.9%) zerosZeros
90세이상 has 888 (60.9%) zerosZeros

Reproduction

Analysis started2023-12-12 14:19:44.444375
Analysis finished2023-12-12 14:19:49.274226
Duration4.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군명
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing1441
Missing (%)98.9%
Memory size11.5 KiB
2023-12-12T23:19:49.404710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8125
Min length2

Characters and Unicode

Total characters45
Distinct characters27
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

Unique16 ?
Unique (%)100.0%

Sample

1st row중구
2nd row서구
3rd row동구
4th row영도구
5th row부산진구
ValueCountFrequency (%)
북구 1
 
6.2%
서구 1
 
6.2%
동구 1
 
6.2%
영도구 1
 
6.2%
부산진구 1
 
6.2%
동래구 1
 
6.2%
남구 1
 
6.2%
사하구 1
 
6.2%
중구 1
 
6.2%
금정구 1
 
6.2%
Other values (6) 6
37.5%
2023-12-12T23:19:49.706793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 15
33.3%
서 2
 
4.4%
동 2
 
4.4%
영 2
 
4.4%
사 2
 
4.4%
북 1
 
2.2%
강 1
 
2.2%
장 1
 
2.2%
기 1
 
2.2%
대 1
 
2.2%
Other values (17) 17
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 15
33.3%
서 2
 
4.4%
동 2
 
4.4%
영 2
 
4.4%
사 2
 
4.4%
북 1
 
2.2%
강 1
 
2.2%
장 1
 
2.2%
기 1
 
2.2%
대 1
 
2.2%
Other values (17) 17
37.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 15
33.3%
서 2
 
4.4%
동 2
 
4.4%
영 2
 
4.4%
사 2
 
4.4%
북 1
 
2.2%
강 1
 
2.2%
장 1
 
2.2%
기 1
 
2.2%
대 1
 
2.2%
Other values (17) 17
37.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 15
33.3%
서 2
 
4.4%
동 2
 
4.4%
영 2
 
4.4%
사 2
 
4.4%
북 1
 
2.2%
강 1
 
2.2%
장 1
 
2.2%
기 1
 
2.2%
대 1
 
2.2%
Other values (17) 17
37.8%

읍면동
Text

MISSING 

Distinct223
Distinct (%)93.7%
Missing1219
Missing (%)83.7%
Memory size11.5 KiB
2023-12-12T23:19:49.970819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.592437
Min length2

Characters and Unicode

Total characters855
Distinct characters116
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

Unique222 ?
Unique (%)93.3%

Sample

1st row소계
2nd row중구
3rd row중앙동
4th row동광동
5th row대청동
ValueCountFrequency (%)
소계 16
 
6.7%
서1동 1
 
0.4%
금정구 1
 
0.4%
하단1동 1
 
0.4%
하단2동 1
 
0.4%
신평1동 1
 
0.4%
신평2동 1
 
0.4%
장림1동 1
 
0.4%
장림2동 1
 
0.4%
다대1동 1
 
0.4%
Other values (213) 213
89.5%
2023-12-12T23:19:50.392999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 211
24.7%
1 57
 
6.7%
2 53
 
6.2%
3 26
 
3.0%
구 22
 
2.6%
대 17
 
2.0%
소 16
 
1.9%
계 16
 
1.9%
연 15
 
1.8%
부 13
 
1.5%
Other values (106) 409
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 697
81.5%
Decimal Number 158
 
18.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 211
30.3%
구 22
 
3.2%
대 17
 
2.4%
소 16
 
2.3%
계 16
 
2.3%
연 15
 
2.2%
부 13
 
1.9%
천 13
 
1.9%
정 13
 
1.9%
산 12
 
1.7%
Other values (98) 349
50.1%
Decimal Number
ValueCountFrequency (%)
1 57
36.1%
2 53
33.5%
3 26
16.5%
4 13
 
8.2%
5 4
 
2.5%
6 3
 
1.9%
8 1
 
0.6%
9 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 697
81.5%
Common 158
 
18.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 211
30.3%
구 22
 
3.2%
대 17
 
2.4%
소 16
 
2.3%
계 16
 
2.3%
연 15
 
2.2%
부 13
 
1.9%
천 13
 
1.9%
정 13
 
1.9%
산 12
 
1.7%
Other values (98) 349
50.1%
Common
ValueCountFrequency (%)
1 57
36.1%
2 53
33.5%
3 26
16.5%
4 13
 
8.2%
5 4
 
2.5%
6 3
 
1.9%
8 1
 
0.6%
9 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 697
81.5%
ASCII 158
 
18.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 211
30.3%
구 22
 
3.2%
대 17
 
2.4%
소 16
 
2.3%
계 16
 
2.3%
연 15
 
2.2%
부 13
 
1.9%
천 13
 
1.9%
정 13
 
1.9%
산 12
 
1.7%
Other values (98) 349
50.1%
ASCII
ValueCountFrequency (%)
1 57
36.1%
2 53
33.5%
3 26
16.5%
4 13
 
8.2%
5 4
 
2.5%
6 3
 
1.9%
8 1
 
0.6%
9 1
 
0.6%

수급자구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
<NA>
701 
일반수급자가구
219 
조건부수급자가구
206 
특례수급자가구
201 
시설수급자
129 

Length

Max length8
Median length7
Mean length5.5175017
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row시설수급자
3rd row일반수급자가구
4th row<NA>
5th row조건부수급자가구

Common Values

ValueCountFrequency (%)
<NA> 701
48.1%
일반수급자가구 219
 
15.0%
조건부수급자가구 206
 
14.1%
특례수급자가구 201
 
13.8%
시설수급자 129
 
8.9%
기타 1
 
0.1%

Length

2023-12-12T23:19:50.559434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:19:50.691721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 701
48.1%
일반수급자가구 219
 
15.0%
조건부수급자가구 206
 
14.1%
특례수급자가구 201
 
13.8%
시설수급자 129
 
8.9%
기타 1
 
0.1%

성별
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
여성
739 
남성
702 
<NA>
 
16

Length

Max length4
Median length2
Mean length2.0219629
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row여성
3rd row남성
4th row여성
5th row남성

Common Values

ValueCountFrequency (%)
여성 739
50.7%
남성 702
48.2%
<NA> 16
 
1.1%

Length

2023-12-12T23:19:50.830730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:19:51.032276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여성 739
50.7%
남성 702
48.2%
na 16
 
1.1%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct463
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.68977
Minimum1
Maximum24877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-12T23:19:51.154849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median41
Q3227
95-th percentile683
Maximum24877
Range24876
Interquartile range (IQR)219

Descriptive statistics

Standard deviation1621.2596
Coefficient of variation (CV)5.2015168
Kurtosis137.70099
Mean311.68977
Median Absolute Deviation (MAD)39
Skewness11.150788
Sum454132
Variance2628482.6
MonotonicityNot monotonic
2023-12-12T23:19:51.318663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 107
 
7.3%
4 48
 
3.3%
2 48
 
3.3%
3 48
 
3.3%
6 39
 
2.7%
5 38
 
2.6%
8 35
 
2.4%
10 32
 
2.2%
11 30
 
2.1%
7 28
 
1.9%
Other values (453) 1004
68.9%
ValueCountFrequency (%)
1 107
7.3%
2 48
3.3%
3 48
3.3%
4 48
3.3%
5 38
 
2.6%
6 39
 
2.7%
7 28
 
1.9%
8 35
 
2.4%
9 23
 
1.6%
10 32
 
2.2%
ValueCountFrequency (%)
24877 1
0.1%
23291 1
0.1%
23005 1
0.1%
22938 1
0.1%
15613 1
0.1%
14511 1
0.1%
13127 1
0.1%
13016 1
0.1%
12661 1
0.1%
12012 1
0.1%

65~69세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct148
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.032258
Minimum0
Maximum2735
Zeros455
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-12T23:19:51.468785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q322
95-th percentile88.2
Maximum2735
Range2735
Interquartile range (IQR)22

Descriptive statistics

Standard deviation182.57623
Coefficient of variation (CV)5.2116604
Kurtosis131.57773
Mean35.032258
Median Absolute Deviation (MAD)2
Skewness10.862468
Sum51042
Variance33334.081
MonotonicityNot monotonic
2023-12-12T23:19:51.611998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 455
31.2%
1 224
15.4%
2 117
 
8.0%
3 79
 
5.4%
4 52
 
3.6%
6 26
 
1.8%
5 25
 
1.7%
7 21
 
1.4%
8 14
 
1.0%
32 13
 
0.9%
Other values (138) 431
29.6%
ValueCountFrequency (%)
0 455
31.2%
1 224
15.4%
2 117
 
8.0%
3 79
 
5.4%
4 52
 
3.6%
5 25
 
1.7%
6 26
 
1.8%
7 21
 
1.4%
8 14
 
1.0%
9 8
 
0.5%
ValueCountFrequency (%)
2735 1
0.1%
2621 1
0.1%
2594 1
0.1%
2490 1
0.1%
1782 1
0.1%
1665 1
0.1%
1534 1
0.1%
1465 1
0.1%
1459 1
0.1%
1333 1
0.1%

70~74세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct139
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.142073
Minimum0
Maximum2277
Zeros569
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-12T23:19:51.760158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q318
95-th percentile76
Maximum2277
Range2277
Interquartile range (IQR)18

Descriptive statistics

Standard deviation152.33124
Coefficient of variation (CV)5.2271931
Kurtosis130.00498
Mean29.142073
Median Absolute Deviation (MAD)1
Skewness10.766214
Sum42460
Variance23204.807
MonotonicityNot monotonic
2023-12-12T23:19:51.915458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 569
39.1%
1 231
15.9%
2 117
 
8.0%
3 44
 
3.0%
4 28
 
1.9%
5 22
 
1.5%
6 16
 
1.1%
35 14
 
1.0%
29 12
 
0.8%
40 11
 
0.8%
Other values (129) 393
27.0%
ValueCountFrequency (%)
0 569
39.1%
1 231
15.9%
2 117
 
8.0%
3 44
 
3.0%
4 28
 
1.9%
5 22
 
1.5%
6 16
 
1.1%
7 6
 
0.4%
8 7
 
0.5%
9 5
 
0.3%
ValueCountFrequency (%)
2277 1
0.1%
2197 1
0.1%
2167 1
0.1%
2046 1
0.1%
1531 1
0.1%
1291 1
0.1%
1280 1
0.1%
1159 1
0.1%
1141 1
0.1%
1129 1
0.1%

75~79세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.87234
Minimum0
Maximum2089
Zeros590
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-12T23:19:52.046851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314
95-th percentile68.2
Maximum2089
Range2089
Interquartile range (IQR)14

Descriptive statistics

Standard deviation135.61007
Coefficient of variation (CV)5.2415078
Kurtosis126.88516
Mean25.87234
Median Absolute Deviation (MAD)1
Skewness10.642104
Sum37696
Variance18390.092
MonotonicityNot monotonic
2023-12-12T23:19:52.178423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 590
40.5%
1 234
 
16.1%
2 104
 
7.1%
3 54
 
3.7%
4 26
 
1.8%
5 14
 
1.0%
9 13
 
0.9%
27 12
 
0.8%
44 11
 
0.8%
6 11
 
0.8%
Other values (119) 388
26.6%
ValueCountFrequency (%)
0 590
40.5%
1 234
 
16.1%
2 104
 
7.1%
3 54
 
3.7%
4 26
 
1.8%
5 14
 
1.0%
6 11
 
0.8%
7 6
 
0.4%
8 11
 
0.8%
9 13
 
0.9%
ValueCountFrequency (%)
2089 1
0.1%
1938 1
0.1%
1811 1
0.1%
1785 1
0.1%
1266 1
0.1%
1216 1
0.1%
1204 1
0.1%
1136 1
0.1%
1076 1
0.1%
1015 1
0.1%

80~89세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct165
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.202471
Minimum0
Maximum3020
Zeros435
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-12T23:19:52.339128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q319
95-th percentile111.2
Maximum3020
Range3020
Interquartile range (IQR)19

Descriptive statistics

Standard deviation206.54397
Coefficient of variation (CV)5.2686468
Kurtosis128.45331
Mean39.202471
Median Absolute Deviation (MAD)2
Skewness10.722107
Sum57118
Variance42660.413
MonotonicityNot monotonic
2023-12-12T23:19:52.484511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
29.9%
1 263
18.1%
2 130
 
8.9%
3 60
 
4.1%
4 53
 
3.6%
5 29
 
2.0%
8 16
 
1.1%
6 15
 
1.0%
7 14
 
1.0%
34 12
 
0.8%
Other values (155) 430
29.5%
ValueCountFrequency (%)
0 435
29.9%
1 263
18.1%
2 130
 
8.9%
3 60
 
4.1%
4 53
 
3.6%
5 29
 
2.0%
6 15
 
1.0%
7 14
 
1.0%
8 16
 
1.1%
9 8
 
0.5%
ValueCountFrequency (%)
3020 1
0.1%
2920 1
0.1%
2915 1
0.1%
2891 1
0.1%
1903 1
0.1%
1893 1
0.1%
1790 1
0.1%
1711 1
0.1%
1563 1
0.1%
1541 1
0.1%

90세이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.969801
Minimum0
Maximum450
Zeros888
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-12T23:19:52.632346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile19
Maximum450
Range450
Interquartile range (IQR)2

Descriptive statistics

Standard deviation31.350166
Coefficient of variation (CV)5.2514591
Kurtosis124.5337
Mean5.969801
Median Absolute Deviation (MAD)0
Skewness10.523549
Sum8698
Variance982.83288
MonotonicityNot monotonic
2023-12-12T23:19:52.783066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 888
60.9%
1 178
 
12.2%
2 70
 
4.8%
3 40
 
2.7%
4 24
 
1.6%
6 19
 
1.3%
5 16
 
1.1%
11 16
 
1.1%
16 15
 
1.0%
9 15
 
1.0%
Other values (49) 176
 
12.1%
ValueCountFrequency (%)
0 888
60.9%
1 178
 
12.2%
2 70
 
4.8%
3 40
 
2.7%
4 24
 
1.6%
5 16
 
1.1%
6 19
 
1.3%
7 15
 
1.0%
8 11
 
0.8%
9 15
 
1.0%
ValueCountFrequency (%)
450 1
0.1%
448 1
0.1%
436 1
0.1%
429 1
0.1%
331 1
0.1%
263 1
0.1%
253 1
0.1%
242 2
0.1%
234 1
0.1%
218 1
0.1%

Interactions

2023-12-12T23:19:48.354782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:44.918936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.492758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:46.129234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.050362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.654217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.441876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:44.997556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.577310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:46.249489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.165347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.798139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.535798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.127813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.668510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:46.345291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.264248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.926240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.614670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.209256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.765476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:46.433661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.352378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.022710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.705662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.306157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.893669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:46.829079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.452018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.137818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.794081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.407900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:45.986357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:46.954582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:47.543473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:19:48.266873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:19:53.187378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구군명수급자구분성별합계65~69세70~74세75~79세80~89세90세이상
구군명1.000NaNNaN1.0001.0001.0001.0001.0001.000
수급자구분NaN1.0000.347NaN0.0000.000NaNNaNNaN
성별NaN0.3471.0000.0000.0000.0570.0380.0310.034
합계1.000NaN0.0001.0000.9390.9510.9230.9260.976
65~69세1.0000.0000.0000.9391.0000.9580.9350.9740.904
70~74세1.0000.0000.0570.9510.9581.0000.9850.9240.911
75~79세1.000NaN0.0380.9230.9350.9851.0000.9400.915
80~89세1.000NaN0.0310.9260.9740.9240.9401.0000.915
90세이상1.000NaN0.0340.9760.9040.9110.9150.9151.000
2023-12-12T23:19:53.339139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별수급자구분
성별1.0000.422
수급자구분0.4221.000
2023-12-12T23:19:53.446882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계65~69세70~74세75~79세80~89세90세이상수급자구분성별
합계1.0000.9020.8180.7810.7640.6691.0000.000
65~69세0.9021.0000.8140.7780.7350.6580.0000.000
70~74세0.8180.8141.0000.8140.7880.7030.0000.036
75~79세0.7810.7780.8141.0000.8180.7331.0000.063
80~89세0.7640.7350.7880.8181.0000.7601.0000.051
90세이상0.6690.6580.7030.7330.7601.0001.0000.057
수급자구분1.0000.0000.0001.0001.0001.0001.0000.422
성별0.0000.0000.0360.0630.0510.0570.4221.000

Missing values

2023-12-12T23:19:48.930080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:19:49.069841image/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.
2023-12-12T23:19:49.199666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구군명읍면동수급자구분성별합계65~69세70~74세75~79세80~89세90세이상
0중구소계<NA><NA>438664451641957692
1<NA>중구시설수급자여성300021
2<NA>중앙동일반수급자가구남성761912590
3<NA><NA><NA>여성7312101184
4<NA><NA>조건부수급자가구남성1210000
5<NA><NA><NA>여성900000
6<NA><NA>시설수급자남성400201
7<NA><NA><NA>여성500031
8<NA><NA>특례수급자가구남성400110
9<NA><NA><NA>여성301000
구군명읍면동수급자구분성별합계65~69세70~74세75~79세80~89세90세이상
1447<NA><NA>특례수급자가구남성100010
1448<NA><NA><NA>여성100100
1449<NA>일광면일반수급자가구남성100000
1450<NA><NA><NA>여성100000
1451<NA>철마면일반수급자가구남성296414131316
1452<NA><NA><NA>여성3876164467910
1453<NA><NA>조건부수급자가구남성4440020
1454<NA><NA><NA>여성7132120
1455<NA><NA>시설수급자여성200010
1456<NA><NA>특례수급자가구여성200000

Duplicate rows

Most frequently occurring

구군명읍면동수급자구분성별합계65~69세70~74세75~79세80~89세90세이상# duplicates
0<NA><NA>시설수급자남성10000020
16<NA><NA>특례수급자가구남성10000011
7<NA><NA>시설수급자여성1000009
35<NA><NA><NA>여성1000009
37<NA><NA><NA>여성1000109
1<NA><NA>시설수급자남성1000107
9<NA><NA>시설수급자여성1000106
19<NA><NA>특례수급자가구남성2000006
22<NA><NA>특례수급자가구남성3000106
23<NA><NA>특례수급자가구남성4000006