Overview

Dataset statistics

Number of variables12
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory110.5 B

Variable types

Categorical2
Text1
Numeric8
DateTime1

Dataset

Description대전광역시 대덕구 동별/세대별 기초생활수급자 현황 데이터로 동별, 일반수급자, 조건부수급자, 특례수급자 항목으로 구성되어 있습니다.
URLhttps://www.data.go.kr/data/15113615/fileData.do

Alerts

구별 has constant value ""Constant
데이터기준일자 has constant value ""Constant
총수급자_가구수 is highly overall correlated with 총수급자_수급권자수 and 5 other fieldsHigh correlation
총수급자_수급권자수 is highly overall correlated with 총수급자_가구수 and 5 other fieldsHigh correlation
일반수급자_가구수 is highly overall correlated with 총수급자_가구수 and 5 other fieldsHigh correlation
일반수급자_수급권자수 is highly overall correlated with 총수급자_가구수 and 6 other fieldsHigh correlation
조건부수급자_가구수 is highly overall correlated with 총수급자_가구수 and 6 other fieldsHigh correlation
조건부수급자_수급권자수 is highly overall correlated with 총수급자_가구수 and 6 other fieldsHigh correlation
특례수급자_가구수 is highly overall correlated with 일반수급자_수급권자수 and 3 other fieldsHigh correlation
특례수급자_수급권자수 is highly overall correlated with 총수급자_가구수 and 6 other fieldsHigh correlation
총수급자_가구수 has unique valuesUnique
총수급자_수급권자수 has unique valuesUnique
일반수급자_가구수 has unique valuesUnique
일반수급자_수급권자수 has unique valuesUnique
조건부수급자_가구수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:19:40.234182
Analysis finished2023-12-12 23:19:46.401693
Duration6.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2021
12 
2022
12 

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 (%)
2021 12
50.0%
2022 12
50.0%

Length

2023-12-13T08:19:46.449926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:19:46.518698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 12
50.0%
2022 12
50.0%

구별
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
대덕구
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대덕구 24
100.0%

Length

2023-12-13T08:19:46.590724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:19:46.658217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대덕구 24
100.0%

동별
Text

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T08:19:46.765839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0833333
Min length3

Characters and Unicode

Total characters74
Distinct characters24
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

Unique0 ?
Unique (%)0.0%

Sample

1st row오정동
2nd row대화동
3rd row회덕동
4th row비래동
5th row송촌동
ValueCountFrequency (%)
오정동 2
8.3%
대화동 2
8.3%
회덕동 2
8.3%
비래동 2
8.3%
송촌동 2
8.3%
중리동 2
8.3%
신탄진동 2
8.3%
석봉동 2
8.3%
덕암동 2
8.3%
목상동 2
8.3%
Other values (2) 4
16.7%
2023-12-13T08:19:46.984613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
32.4%
법 4
 
5.4%
덕 4
 
5.4%
오 2
 
2.7%
신 2
 
2.7%
1 2
 
2.7%
상 2
 
2.7%
목 2
 
2.7%
암 2
 
2.7%
봉 2
 
2.7%
Other values (14) 28
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
94.6%
Decimal Number 4
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
34.3%
법 4
 
5.7%
덕 4
 
5.7%
오 2
 
2.9%
신 2
 
2.9%
상 2
 
2.9%
목 2
 
2.9%
암 2
 
2.9%
봉 2
 
2.9%
석 2
 
2.9%
Other values (12) 24
34.3%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
94.6%
Common 4
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
34.3%
법 4
 
5.7%
덕 4
 
5.7%
오 2
 
2.9%
신 2
 
2.9%
상 2
 
2.9%
목 2
 
2.9%
암 2
 
2.9%
봉 2
 
2.9%
석 2
 
2.9%
Other values (12) 24
34.3%
Common
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
94.6%
ASCII 4
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
34.3%
법 4
 
5.7%
덕 4
 
5.7%
오 2
 
2.9%
신 2
 
2.9%
상 2
 
2.9%
목 2
 
2.9%
암 2
 
2.9%
봉 2
 
2.9%
석 2
 
2.9%
Other values (12) 24
34.3%
ASCII
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

총수급자_가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean647.04167
Minimum123
Maximum1430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:47.078477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile166.5
Q1374
median512.5
Q3817
95-th percentile1402.05
Maximum1430
Range1307
Interquartile range (IQR)443

Descriptive statistics

Standard deviation417.58191
Coefficient of variation (CV)0.64537097
Kurtosis-0.65758694
Mean647.04167
Median Absolute Deviation (MAD)151.5
Skewness0.86284545
Sum15529
Variance174374.65
MonotonicityNot monotonic
2023-12-13T08:19:47.170188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
635 1
 
4.2%
371 1
 
4.2%
1430 1
 
4.2%
1241 1
 
4.2%
153 1
 
4.2%
482 1
 
4.2%
257 1
 
4.2%
384 1
 
4.2%
1306 1
 
4.2%
577 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
123 1
4.2%
153 1
4.2%
243 1
4.2%
257 1
4.2%
351 1
4.2%
371 1
4.2%
375 1
4.2%
384 1
4.2%
413 1
4.2%
419 1
4.2%
ValueCountFrequency (%)
1430 1
4.2%
1419 1
4.2%
1306 1
4.2%
1258 1
4.2%
1241 1
4.2%
1204 1
4.2%
688 1
4.2%
635 1
4.2%
616 1
4.2%
577 1
4.2%

총수급자_수급권자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.33333
Minimum208
Maximum2048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:47.257051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum208
5-th percentile268.5
Q1557.75
median833.5
Q31181.25
95-th percentile1917.7
Maximum2048
Range1840
Interquartile range (IQR)623.5

Descriptive statistics

Standard deviation569.77743
Coefficient of variation (CV)0.59955534
Kurtosis-0.76203888
Mean950.33333
Median Absolute Deviation (MAD)282
Skewness0.7105881
Sum22808
Variance324646.32
MonotonicityNot monotonic
2023-12-13T08:19:47.351191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
891 1
 
4.2%
564 1
 
4.2%
1814 1
 
4.2%
1656 1
 
4.2%
255 1
 
4.2%
776 1
 
4.2%
364 1
 
4.2%
539 1
 
4.2%
2048 1
 
4.2%
1023 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
208 1
4.2%
255 1
4.2%
345 1
4.2%
364 1
4.2%
491 1
4.2%
539 1
4.2%
564 1
4.2%
566 1
4.2%
594 1
4.2%
602 1
4.2%
ValueCountFrequency (%)
2048 1
4.2%
1936 1
4.2%
1814 1
4.2%
1802 1
4.2%
1704 1
4.2%
1656 1
4.2%
1023 1
4.2%
995 1
4.2%
975 1
4.2%
966 1
4.2%

일반수급자_가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.5
Minimum111
Maximum1247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:47.450691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile143.5
Q1306.5
median436.5
Q3658.25
95-th percentile1208.55
Maximum1247
Range1136
Interquartile range (IQR)351.75

Descriptive statistics

Standard deviation356.11454
Coefficient of variation (CV)0.6516277
Kurtosis-0.5028976
Mean546.5
Median Absolute Deviation (MAD)133.5
Skewness0.92858024
Sum13116
Variance126817.57
MonotonicityNot monotonic
2023-12-13T08:19:47.547994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
520 1
 
4.2%
294 1
 
4.2%
1247 1
 
4.2%
1087 1
 
4.2%
133 1
 
4.2%
424 1
 
4.2%
221 1
 
4.2%
328 1
 
4.2%
1037 1
 
4.2%
482 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
111 1
4.2%
133 1
4.2%
203 1
4.2%
221 1
4.2%
294 1
4.2%
296 1
4.2%
310 1
4.2%
328 1
4.2%
360 1
4.2%
368 1
4.2%
ValueCountFrequency (%)
1247 1
4.2%
1227 1
4.2%
1104 1
4.2%
1087 1
4.2%
1037 1
4.2%
974 1
4.2%
553 1
4.2%
520 1
4.2%
510 1
4.2%
482 1
4.2%

일반수급자_수급권자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean766.20833
Minimum185
Maximum1573
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:47.654338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum185
5-th percentile232.1
Q1429.75
median671.5
Q3967
95-th percentile1514.25
Maximum1573
Range1388
Interquartile range (IQR)537.25

Descriptive statistics

Standard deviation454.96316
Coefficient of variation (CV)0.59378519
Kurtosis-0.84877259
Mean766.20833
Median Absolute Deviation (MAD)243
Skewness0.71954746
Sum18389
Variance206991.48
MonotonicityNot monotonic
2023-12-13T08:19:47.786695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
704 1
 
4.2%
431 1
 
4.2%
1510 1
 
4.2%
1375 1
 
4.2%
224 1
 
4.2%
639 1
 
4.2%
304 1
 
4.2%
426 1
 
4.2%
1573 1
 
4.2%
831 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
185 1
4.2%
224 1
4.2%
278 1
4.2%
304 1
4.2%
400 1
4.2%
426 1
4.2%
431 1
4.2%
452 1
4.2%
507 1
4.2%
513 1
4.2%
ValueCountFrequency (%)
1573 1
4.2%
1515 1
4.2%
1510 1
4.2%
1473 1
4.2%
1404 1
4.2%
1375 1
4.2%
831 1
4.2%
765 1
4.2%
760 1
4.2%
759 1
4.2%

조건부수급자_가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.5
Minimum10
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:47.887292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile19.4
Q150.75
median82
Q3136
95-th percentile213.75
Maximum259
Range249
Interquartile range (IQR)85.25

Descriptive statistics

Standard deviation65.197359
Coefficient of variation (CV)0.6756203
Kurtosis0.28337404
Mean96.5
Median Absolute Deviation (MAD)32.5
Skewness0.93200423
Sum2316
Variance4250.6957
MonotonicityNot monotonic
2023-12-13T08:19:48.004201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
113 1
 
4.2%
73 1
 
4.2%
177 1
 
4.2%
149 1
 
4.2%
17 1
 
4.2%
55 1
 
4.2%
33 1
 
4.2%
53 1
 
4.2%
259 1
 
4.2%
92 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
10 1
4.2%
17 1
4.2%
33 1
4.2%
37 1
4.2%
49 1
4.2%
50 1
4.2%
51 1
4.2%
53 1
4.2%
54 1
4.2%
55 1
4.2%
ValueCountFrequency (%)
259 1
4.2%
219 1
4.2%
184 1
4.2%
177 1
4.2%
149 1
4.2%
148 1
4.2%
132 1
4.2%
113 1
4.2%
105 1
4.2%
104 1
4.2%

조건부수급자_수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.08333
Minimum20
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:48.109052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile32.35
Q187.5
median159
Q3242.5
95-th percentile391.55
Maximum460
Range440
Interquartile range (IQR)155

Descriptive statistics

Standard deviation116.75166
Coefficient of variation (CV)0.65194038
Kurtosis0.069244673
Mean179.08333
Median Absolute Deviation (MAD)73
Skewness0.78029734
Sum4298
Variance13630.949
MonotonicityNot monotonic
2023-12-13T08:19:48.219859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
110 2
 
8.3%
185 1
 
4.2%
298 1
 
4.2%
274 1
 
4.2%
28 1
 
4.2%
133 1
 
4.2%
57 1
 
4.2%
460 1
 
4.2%
188 1
 
4.2%
212 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
20 1
4.2%
28 1
4.2%
57 1
4.2%
64 1
4.2%
82 1
4.2%
86 1
4.2%
88 1
4.2%
109 1
4.2%
110 2
8.3%
129 1
4.2%
ValueCountFrequency (%)
460 1
4.2%
404 1
4.2%
321 1
4.2%
298 1
4.2%
294 1
4.2%
274 1
4.2%
232 1
4.2%
217 1
4.2%
212 1
4.2%
197 1
4.2%

특례수급자_가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0416667
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:48.320208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.75
median3
Q34.25
95-th percentile9.7
Maximum11
Range10
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.5277446
Coefficient of variation (CV)0.62542134
Kurtosis2.1835277
Mean4.0416667
Median Absolute Deviation (MAD)1
Skewness1.6052896
Sum97
Variance6.3894928
MonotonicityNot monotonic
2023-12-13T08:19:48.417145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 8
33.3%
2 5
20.8%
4 4
16.7%
6 2
 
8.3%
11 1
 
4.2%
8 1
 
4.2%
1 1
 
4.2%
10 1
 
4.2%
5 1
 
4.2%
ValueCountFrequency (%)
1 1
 
4.2%
2 5
20.8%
3 8
33.3%
4 4
16.7%
5 1
 
4.2%
6 2
 
8.3%
8 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
ValueCountFrequency (%)
11 1
 
4.2%
10 1
 
4.2%
8 1
 
4.2%
6 2
 
8.3%
5 1
 
4.2%
4 4
16.7%
3 8
33.3%
2 5
20.8%
1 1
 
4.2%

특례수급자_수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0416667
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T08:19:48.520715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q13
median4
Q35.25
95-th percentile13.95
Maximum17
Range16
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation3.7239433
Coefficient of variation (CV)0.73863338
Kurtosis5.6254577
Mean5.0416667
Median Absolute Deviation (MAD)1
Skewness2.3614314
Sum121
Variance13.867754
MonotonicityNot monotonic
2023-12-13T08:19:48.621471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 7
29.2%
3 7
29.2%
5 2
 
8.3%
6 2
 
8.3%
2 1
 
4.2%
17 1
 
4.2%
8 1
 
4.2%
1 1
 
4.2%
15 1
 
4.2%
7 1
 
4.2%
ValueCountFrequency (%)
1 1
 
4.2%
2 1
 
4.2%
3 7
29.2%
4 7
29.2%
5 2
 
8.3%
6 2
 
8.3%
7 1
 
4.2%
8 1
 
4.2%
15 1
 
4.2%
17 1
 
4.2%
ValueCountFrequency (%)
17 1
 
4.2%
15 1
 
4.2%
8 1
 
4.2%
7 1
 
4.2%
6 2
 
8.3%
5 2
 
8.3%
4 7
29.2%
3 7
29.2%
2 1
 
4.2%
1 1
 
4.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2023-05-01 00:00:00
Maximum2023-05-01 00:00:00
2023-12-13T08:19:48.728843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:48.820242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:19:45.398897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:40.520222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.394418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.190952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.898475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.606682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.186234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.807731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.474661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:40.601120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.478764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.275773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.999446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.681918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.259088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.885890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.553630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:40.683573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.553674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.370825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.092803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.759955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.331569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.958191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.623272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:40.762615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.629977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.451900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.168423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.832602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.429868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.024229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.701827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:40.842035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.708106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.533543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.255840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.902937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.509338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.094438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.765607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.175328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.857326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.619725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.345577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.969162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.576236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.171062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.835076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.242749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.957979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.706574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.431450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.031203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.643464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.257806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.896076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:41.319592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.058650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:42.785897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:43.510152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.099229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:44.719343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:19:45.323522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:19:48.896224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도동별총수급자_가구수총수급자_수급권자수일반수급자_가구수일반수급자_수급권자수조건부수급자_가구수조건부수급자_수급권자수특례수급자_가구수특례수급자_수급권자수
년도1.0000.0000.0000.0000.0000.0000.0000.0000.3360.000
동별0.0001.0000.9250.9510.9490.9690.9640.8450.6580.659
총수급자_가구수0.0000.9251.0000.8360.9670.9600.8730.7500.5470.636
총수급자_수급권자수0.0000.9510.8361.0000.9020.9260.8570.9040.8290.680
일반수급자_가구수0.0000.9490.9670.9021.0000.9650.9290.8710.7920.885
일반수급자_수급권자수0.0000.9690.9600.9260.9651.0000.8260.7800.7470.761
조건부수급자_가구수0.0000.9640.8730.8570.9290.8261.0000.9770.9120.846
조건부수급자_수급권자수0.0000.8450.7500.9040.8710.7800.9771.0000.9390.858
특례수급자_가구수0.3360.6580.5470.8290.7920.7470.9120.9391.0000.940
특례수급자_수급권자수0.0000.6590.6360.6800.8850.7610.8460.8580.9401.000
2023-12-13T08:19:49.037823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총수급자_가구수총수급자_수급권자수일반수급자_가구수일반수급자_수급권자수조건부수급자_가구수조건부수급자_수급권자수특례수급자_가구수특례수급자_수급권자수년도
총수급자_가구수1.0000.9710.9970.9640.9400.9420.4460.5940.000
총수급자_수급권자수0.9711.0000.9620.9970.9430.9580.4950.6680.000
일반수급자_가구수0.9970.9621.0000.9550.9280.9300.4400.5930.000
일반수급자_수급권자수0.9640.9970.9551.0000.9370.9550.5010.6740.000
조건부수급자_가구수0.9400.9430.9280.9371.0000.9840.5440.6610.000
조건부수급자_수급권자수0.9420.9580.9300.9550.9841.0000.5350.6810.000
특례수급자_가구수0.4460.4950.4400.5010.5440.5351.0000.8510.126
특례수급자_수급권자수0.5940.6680.5930.6740.6610.6810.8511.0000.000
년도0.0000.0000.0000.0000.0000.0000.1260.0001.000

Missing values

2023-12-13T08:19:45.997384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:19:46.347669image/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

년도구별동별총수급자_가구수총수급자_수급권자수일반수급자_가구수일반수급자_수급권자수조건부수급자_가구수조건부수급자_수급권자수특례수급자_가구수특례수급자_수급권자수데이터기준일자
02021대덕구오정동635891520704113185222023-05-01
12021대덕구대화동37556631045261110442023-05-01
22021대덕구회덕동4135943605074982452023-05-01
32021대덕구비래동573946466725105217242023-05-01
42021대덕구송촌동54396644976591197342023-05-01
52021대덕구중리동12041936974151521940411172023-05-01
62021대덕구신탄진동3514912964005186452023-05-01
72021대덕구석봉동2433452032783764332023-05-01
82021대덕구덕암동46874841263654109232023-05-01
92021대덕구목상동1232081111851020232023-05-01
년도구별동별총수급자_가구수총수급자_수급권자수일반수급자_가구수일반수급자_수급권자수조건부수급자_가구수조건부수급자_수급권자수특례수급자_가구수특례수급자_수급권자수데이터기준일자
142022대덕구회덕동4196023685135088112023-05-01
152022대덕구비래동616975510759104212242023-05-01
162022대덕구송촌동577102348283192188342023-05-01
172022대덕구중리동130620481037157325946010152023-05-01
182022대덕구신탄진동38453932842653110332023-05-01
192022대덕구석봉동2573642213043357332023-05-01
202022대덕구덕암동48277642463955133342023-05-01
212022대덕구목상동1532551332241728332023-05-01
222022대덕구법1동1241165610871375149274572023-05-01
232022대덕구법2동1430181412471510177298662023-05-01