Overview

Dataset statistics

Number of variables16
Number of observations182
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.2 KiB
Average record size in memory141.7 B

Variable types

Numeric11
Categorical4
DateTime1

Dataset

Description대구광역시 남구 연도별 기초생활보장수급자 수에 대한 데이터로 동 및 조건부, 특례, 기타별 가구수, 수급권자수 등의 항목을 제공합니다.
Author대구광역시 남구
URLhttps://www.data.go.kr/data/15034114/fileData.do

Alerts

시군구 has constant value ""Constant
데이터기준일자 has constant value ""Constant
기타-가구수 is highly overall correlated with 기타-수급권자수High correlation
기타-수급권자수 is highly overall correlated with 기타-가구수High correlation
날짜 is highly overall correlated with 총 가구수 and 2 other fieldsHigh correlation
총 가구수 is highly overall correlated with 날짜 and 6 other fieldsHigh correlation
총 수급권자수 is highly overall correlated with 총 가구수 and 4 other fieldsHigh correlation
일반수급자-가구수 is highly overall correlated with 날짜 and 6 other fieldsHigh correlation
일반수급자-수급권자수 is highly overall correlated with 총 가구수 and 4 other fieldsHigh correlation
조건부수급자-가구수 is highly overall correlated with 날짜 and 5 other fieldsHigh correlation
조건부수급자-수급권자수 is highly overall correlated with 총 가구수 and 4 other fieldsHigh correlation
특례수급자-가구수 is highly overall correlated with 총 가구수 and 2 other fieldsHigh correlation
특례수급자-수급권자수 is highly overall correlated with 특례수급자-가구수High correlation
시설수급자-가구수 is highly overall correlated with 시설수급자-수급권자수High correlation
시설수급자-수급권자수 is highly overall correlated with 시설수급자-가구수High correlation
기타-가구수 is highly imbalanced (64.9%)Imbalance
기타-수급권자수 is highly imbalanced (75.4%)Imbalance
총 가구수 has 13 (7.1%) zerosZeros
일반수급자-가구수 has 13 (7.1%) zerosZeros
조건부수급자-가구수 has 13 (7.1%) zerosZeros
특례수급자-가구수 has 13 (7.1%) zerosZeros
시설수급자-가구수 has 73 (40.1%) zerosZeros
시설수급자-수급권자수 has 64 (35.2%) zerosZeros

Reproduction

Analysis started2023-12-12 03:24:34.862825
Analysis finished2023-12-12 03:24:51.805717
Duration16.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5
Minimum2010
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:51.896327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2016.5
Q32020
95-th percentile2023
Maximum2023
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0422493
Coefficient of variation (CV)0.0020045868
Kurtosis-1.2125781
Mean2016.5
Median Absolute Deviation (MAD)3.5
Skewness0
Sum367003
Variance16.339779
MonotonicityIncreasing
2023-12-12T12:24:52.075023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2010 13
 
7.1%
2011 13
 
7.1%
2012 13
 
7.1%
2013 13
 
7.1%
2014 13
 
7.1%
2015 13
 
7.1%
2016 13
 
7.1%
2017 13
 
7.1%
2018 13
 
7.1%
2019 13
 
7.1%
Other values (4) 52
28.6%
ValueCountFrequency (%)
2010 13
7.1%
2011 13
7.1%
2012 13
7.1%
2013 13
7.1%
2014 13
7.1%
2015 13
7.1%
2016 13
7.1%
2017 13
7.1%
2018 13
7.1%
2019 13
7.1%
ValueCountFrequency (%)
2023 13
7.1%
2022 13
7.1%
2021 13
7.1%
2020 13
7.1%
2019 13
7.1%
2018 13
7.1%
2017 13
7.1%
2016 13
7.1%
2015 13
7.1%
2014 13
7.1%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
남구
182 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남구 182
100.0%

Length

2023-12-12T12:24:52.236103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:24:52.377091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 182
100.0%

읍면동
Categorical

Distinct13
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
이천동
14 
봉덕1동
14 
봉덕2동
14 
봉덕3동
14 
대명1동
14 
Other values (8)
112 

Length

Max length5
Median length4
Mean length4.0769231
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이천동
2nd row봉덕1동
3rd row봉덕2동
4th row봉덕3동
5th row대명1동

Common Values

ValueCountFrequency (%)
이천동 14
 
7.7%
봉덕1동 14
 
7.7%
봉덕2동 14
 
7.7%
봉덕3동 14
 
7.7%
대명1동 14
 
7.7%
대명2동 14
 
7.7%
대명3동 14
 
7.7%
대명4동 14
 
7.7%
대명5동 14
 
7.7%
대명6동 14
 
7.7%
Other values (3) 42
23.1%

Length

2023-12-12T12:24:52.543380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이천동 14
 
7.7%
봉덕1동 14
 
7.7%
봉덕2동 14
 
7.7%
봉덕3동 14
 
7.7%
대명1동 14
 
7.7%
대명2동 14
 
7.7%
대명3동 14
 
7.7%
대명4동 14
 
7.7%
대명5동 14
 
7.7%
대명6동 14
 
7.7%
Other values (3) 42
23.1%

총 가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct155
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.65385
Minimum0
Maximum1466
Zeros13
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:52.717215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1385.5
median525.5
Q3776
95-th percentile1066.6
Maximum1466
Range1466
Interquartile range (IQR)390.5

Descriptive statistics

Standard deviation296.76135
Coefficient of variation (CV)0.51552049
Kurtosis0.54687658
Mean575.65385
Median Absolute Deviation (MAD)170.5
Skewness0.42653061
Sum104769
Variance88067.299
MonotonicityNot monotonic
2023-12-12T12:24:53.244191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
7.1%
296 2
 
1.1%
371 2
 
1.1%
1067 2
 
1.1%
466 2
 
1.1%
343 2
 
1.1%
1250 2
 
1.1%
409 2
 
1.1%
406 2
 
1.1%
309 2
 
1.1%
Other values (145) 151
83.0%
ValueCountFrequency (%)
0 13
7.1%
281 1
 
0.5%
289 1
 
0.5%
296 2
 
1.1%
307 1
 
0.5%
309 2
 
1.1%
311 1
 
0.5%
322 1
 
0.5%
325 1
 
0.5%
330 1
 
0.5%
ValueCountFrequency (%)
1466 1
0.5%
1441 1
0.5%
1427 1
0.5%
1300 1
0.5%
1272 1
0.5%
1250 2
1.1%
1085 1
0.5%
1067 2
1.1%
1059 1
0.5%
1054 1
0.5%

총 수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct163
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean883.84615
Minimum401
Maximum1964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:53.428768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum401
5-th percentile478.2
Q1587.5
median815
Q31093.25
95-th percentile1494.9
Maximum1964
Range1563
Interquartile range (IQR)505.75

Descriptive statistics

Standard deviation342.71095
Coefficient of variation (CV)0.38774955
Kurtosis0.50112287
Mean883.84615
Median Absolute Deviation (MAD)235.5
Skewness0.9207631
Sum160860
Variance117450.79
MonotonicityNot monotonic
2023-12-12T12:24:53.583954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
634 3
 
1.6%
561 2
 
1.1%
931 2
 
1.1%
562 2
 
1.1%
580 2
 
1.1%
1096 2
 
1.1%
743 2
 
1.1%
1037 2
 
1.1%
531 2
 
1.1%
477 2
 
1.1%
Other values (153) 161
88.5%
ValueCountFrequency (%)
401 1
0.5%
405 1
0.5%
412 1
0.5%
434 1
0.5%
449 1
0.5%
467 1
0.5%
472 1
0.5%
477 2
1.1%
478 1
0.5%
482 1
0.5%
ValueCountFrequency (%)
1964 1
0.5%
1939 1
0.5%
1915 1
0.5%
1812 1
0.5%
1766 1
0.5%
1725 1
0.5%
1714 1
0.5%
1564 1
0.5%
1515 1
0.5%
1496 1
0.5%

일반수급자-가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct147
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.58791
Minimum0
Maximum1300
Zeros13
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:53.759501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1342.25
median465.5
Q3671.75
95-th percentile944.15
Maximum1300
Range1300
Interquartile range (IQR)329.5

Descriptive statistics

Standard deviation260.43828
Coefficient of variation (CV)0.51511968
Kurtosis0.63963987
Mean505.58791
Median Absolute Deviation (MAD)148
Skewness0.44112433
Sum92017
Variance67828.1
MonotonicityNot monotonic
2023-12-12T12:24:53.914090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
7.1%
364 3
 
1.6%
433 3
 
1.6%
521 2
 
1.1%
423 2
 
1.1%
595 2
 
1.1%
656 2
 
1.1%
683 2
 
1.1%
448 2
 
1.1%
252 2
 
1.1%
Other values (137) 149
81.9%
ValueCountFrequency (%)
0 13
7.1%
244 1
 
0.5%
252 2
 
1.1%
255 1
 
0.5%
258 1
 
0.5%
259 1
 
0.5%
269 1
 
0.5%
270 1
 
0.5%
282 1
 
0.5%
286 1
 
0.5%
ValueCountFrequency (%)
1300 1
0.5%
1290 1
0.5%
1284 1
0.5%
1114 1
0.5%
1101 1
0.5%
1090 1
0.5%
1078 1
0.5%
948 1
0.5%
946 1
0.5%
945 1
0.5%

일반수급자-수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean748.04945
Minimum321
Maximum1690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:54.068468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum321
5-th percentile393.7
Q1516.75
median697.5
Q3907
95-th percentile1241.95
Maximum1690
Range1369
Interquartile range (IQR)390.25

Descriptive statistics

Standard deviation285.31822
Coefficient of variation (CV)0.38141626
Kurtosis0.82664328
Mean748.04945
Median Absolute Deviation (MAD)191.5
Skewness0.97373464
Sum136145
Variance81406.489
MonotonicityNot monotonic
2023-12-12T12:24:54.238100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
542 3
 
1.6%
575 2
 
1.1%
476 2
 
1.1%
486 2
 
1.1%
933 2
 
1.1%
742 2
 
1.1%
850 2
 
1.1%
607 2
 
1.1%
1025 2
 
1.1%
508 2
 
1.1%
Other values (154) 161
88.5%
ValueCountFrequency (%)
321 1
0.5%
335 1
0.5%
340 1
0.5%
359 1
0.5%
361 1
0.5%
383 1
0.5%
385 1
0.5%
389 1
0.5%
392 1
0.5%
393 1
0.5%
ValueCountFrequency (%)
1690 1
0.5%
1684 1
0.5%
1665 1
0.5%
1499 1
0.5%
1464 1
0.5%
1453 1
0.5%
1437 1
0.5%
1285 1
0.5%
1270 1
0.5%
1242 1
0.5%

조건부수급자-가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.241758
Minimum0
Maximum150
Zeros13
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:54.420941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124.25
median41
Q369
95-th percentile110.6
Maximum150
Range150
Interquartile range (IQR)44.75

Descriptive statistics

Standard deviation32.516665
Coefficient of variation (CV)0.66034736
Kurtosis-0.054732286
Mean49.241758
Median Absolute Deviation (MAD)21
Skewness0.66801623
Sum8962
Variance1057.3335
MonotonicityNot monotonic
2023-12-12T12:24:54.667391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
7.1%
40 7
 
3.8%
31 7
 
3.8%
20 5
 
2.7%
27 5
 
2.7%
21 4
 
2.2%
22 4
 
2.2%
38 3
 
1.6%
69 3
 
1.6%
65 3
 
1.6%
Other values (75) 128
70.3%
ValueCountFrequency (%)
0 13
7.1%
9 2
 
1.1%
12 1
 
0.5%
13 1
 
0.5%
15 3
 
1.6%
16 2
 
1.1%
17 2
 
1.1%
18 2
 
1.1%
19 2
 
1.1%
20 5
 
2.7%
ValueCountFrequency (%)
150 1
0.5%
131 1
0.5%
129 1
0.5%
128 1
0.5%
126 1
0.5%
125 1
0.5%
124 1
0.5%
117 1
0.5%
115 1
0.5%
111 1
0.5%

조건부수급자-수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.5
Minimum16
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:54.916050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile34
Q155.75
median94.5
Q3143.5
95-th percentile221.75
Maximum262
Range246
Interquartile range (IQR)87.75

Descriptive statistics

Standard deviation59.177862
Coefficient of variation (CV)0.5609276
Kurtosis-0.36505806
Mean105.5
Median Absolute Deviation (MAD)43
Skewness0.686118
Sum19201
Variance3502.0193
MonotonicityNot monotonic
2023-12-12T12:24:55.118635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 7
 
3.8%
66 4
 
2.2%
37 3
 
1.6%
137 3
 
1.6%
77 3
 
1.6%
64 3
 
1.6%
55 3
 
1.6%
71 3
 
1.6%
141 2
 
1.1%
44 2
 
1.1%
Other values (115) 149
81.9%
ValueCountFrequency (%)
16 1
0.5%
21 1
0.5%
24 1
0.5%
28 1
0.5%
29 2
1.1%
31 1
0.5%
32 2
1.1%
34 2
1.1%
35 2
1.1%
36 2
1.1%
ValueCountFrequency (%)
262 1
0.5%
261 1
0.5%
252 1
0.5%
249 1
0.5%
238 1
0.5%
231 1
0.5%
229 2
1.1%
228 1
0.5%
222 1
0.5%
217 1
0.5%

특례수급자-가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.510989
Minimum0
Maximum53
Zeros13
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:55.309233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median13
Q319
95-th percentile38.95
Maximum53
Range53
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.199713
Coefficient of variation (CV)0.72205024
Kurtosis1.2601767
Mean15.510989
Median Absolute Deviation (MAD)5
Skewness1.165767
Sum2823
Variance125.43358
MonotonicityNot monotonic
2023-12-12T12:24:55.464775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
11 15
 
8.2%
0 13
 
7.1%
12 12
 
6.6%
16 10
 
5.5%
15 9
 
4.9%
18 8
 
4.4%
4 8
 
4.4%
9 8
 
4.4%
7 8
 
4.4%
6 7
 
3.8%
Other values (29) 84
46.2%
ValueCountFrequency (%)
0 13
7.1%
1 1
 
0.5%
3 1
 
0.5%
4 8
4.4%
5 3
 
1.6%
6 7
3.8%
7 8
4.4%
8 7
3.8%
9 8
4.4%
10 5
 
2.7%
ValueCountFrequency (%)
53 2
1.1%
49 1
 
0.5%
47 1
 
0.5%
42 3
1.6%
41 1
 
0.5%
40 1
 
0.5%
39 1
 
0.5%
38 2
1.1%
37 3
1.6%
34 1
 
0.5%

특례수급자-수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.67033
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:55.640131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.05
Q113
median20
Q329
95-th percentile52.95
Maximum82
Range81
Interquartile range (IQR)16

Descriptive statistics

Standard deviation15.37671
Coefficient of variation (CV)0.64961959
Kurtosis1.6173669
Mean23.67033
Median Absolute Deviation (MAD)8
Skewness1.2730481
Sum4308
Variance236.4432
MonotonicityNot monotonic
2023-12-12T12:24:55.861399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 13
 
7.1%
13 10
 
5.5%
8 10
 
5.5%
12 9
 
4.9%
15 8
 
4.4%
9 6
 
3.3%
19 6
 
3.3%
28 6
 
3.3%
17 6
 
3.3%
21 5
 
2.7%
Other values (46) 103
56.6%
ValueCountFrequency (%)
1 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%
5 4
 
2.2%
6 3
 
1.6%
7 1
 
0.5%
8 10
5.5%
9 6
3.3%
10 5
2.7%
11 4
 
2.2%
ValueCountFrequency (%)
82 1
0.5%
80 1
0.5%
72 1
0.5%
62 2
1.1%
60 1
0.5%
58 1
0.5%
56 1
0.5%
54 1
0.5%
53 1
0.5%
52 1
0.5%

기타-가구수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
170 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 170
93.4%
1 12
 
6.6%

Length

2023-12-12T12:24:56.121457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:24:56.295258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 170
93.4%
1 12
 
6.6%

기타-수급권자수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
171 
1
 
6
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
94.0%
1 6
 
3.3%
2 5
 
2.7%

Length

2023-12-12T12:24:56.435534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:24:56.548938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 171
94.0%
1 6
 
3.3%
2 5
 
2.7%

시설수급자-가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7417582
Minimum0
Maximum72
Zeros73
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:56.667753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile26.9
Maximum72
Range72
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.337632
Coefficient of variation (CV)1.8004298
Kurtosis12.029212
Mean5.7417582
Median Absolute Deviation (MAD)1
Skewness2.9996126
Sum1045
Variance106.86664
MonotonicityNot monotonic
2023-12-12T12:24:56.817989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 73
40.1%
1 23
 
12.6%
2 15
 
8.2%
3 10
 
5.5%
4 7
 
3.8%
5 7
 
3.8%
20 6
 
3.3%
7 4
 
2.2%
6 4
 
2.2%
19 4
 
2.2%
Other values (19) 29
 
15.9%
ValueCountFrequency (%)
0 73
40.1%
1 23
 
12.6%
2 15
 
8.2%
3 10
 
5.5%
4 7
 
3.8%
5 7
 
3.8%
6 4
 
2.2%
7 4
 
2.2%
8 1
 
0.5%
9 4
 
2.2%
ValueCountFrequency (%)
72 1
0.5%
56 1
0.5%
35 1
0.5%
33 1
0.5%
32 2
1.1%
31 2
1.1%
29 1
0.5%
27 1
0.5%
25 1
0.5%
24 1
0.5%

시설수급자-수급권자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5384615
Minimum0
Maximum72
Zeros64
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T12:24:56.998912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile31
Maximum72
Range72
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.104036
Coefficient of variation (CV)1.6982643
Kurtosis8.7532891
Mean6.5384615
Median Absolute Deviation (MAD)2
Skewness2.6385393
Sum1190
Variance123.29962
MonotonicityNot monotonic
2023-12-12T12:24:57.155073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 64
35.2%
1 26
14.3%
2 15
 
8.2%
3 11
 
6.0%
5 7
 
3.8%
4 7
 
3.8%
20 6
 
3.3%
9 5
 
2.7%
6 4
 
2.2%
7 4
 
2.2%
Other values (21) 33
18.1%
ValueCountFrequency (%)
0 64
35.2%
1 26
14.3%
2 15
 
8.2%
3 11
 
6.0%
4 7
 
3.8%
5 7
 
3.8%
6 4
 
2.2%
7 4
 
2.2%
8 1
 
0.5%
9 5
 
2.7%
ValueCountFrequency (%)
72 1
0.5%
56 1
0.5%
41 1
0.5%
38 1
0.5%
35 2
1.1%
33 1
0.5%
32 2
1.1%
31 2
1.1%
29 1
0.5%
27 1
0.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2023-08-31 00:00:00
Maximum2023-08-31 00:00:00
2023-12-12T12:24:57.280006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:57.394030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:24:49.771148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:35.585576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.709852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:38.342887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.877729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.385592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.629371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.907236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:45.304468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.949287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.296587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:49.915965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:35.708127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.812696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:38.447263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.027764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.501559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.752462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.032713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:45.444735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.043301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.427088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.048252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:35.832585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.912986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:38.587214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.167505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.635475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.892052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.144264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:45.559872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.158969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.541632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.203551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:35.944247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.022396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:38.732726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.321729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.774295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.019190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.264381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:45.691598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.279361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.673162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.321180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.037746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.118908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:38.878464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.451965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.895769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.142896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.376825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.195404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.385057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.835242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.457751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.126117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.222798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.017483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.578089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.999582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.259640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.508615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.358975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.482122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.962802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.604850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.225335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.322942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.176985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.726978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.118259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.358035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.685773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.478015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.606666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:49.109846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.742684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.307021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.441548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.300302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.846842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.215373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.441211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.779242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.564828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.717019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:49.223228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.869250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.393896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.587076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.428836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:40.966955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.331307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.534043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:44.908462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.653685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.827652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:49.360117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:50.999476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.490604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.739344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.577981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.101874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.435260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.672166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:45.069222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.750299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:47.953757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:49.506260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:51.137386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:36.601280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:37.856804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:39.733711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:41.231855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:42.531248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:43.781286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:45.190401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:46.854162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:48.092824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:24:49.651725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:24:57.522136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜읍면동총 가구수총 수급권자수일반수급자-가구수일반수급자-수급권자수조건부수급자-가구수조건부수급자-수급권자수특례수급자-가구수특례수급자-수급권자수기타-가구수기타-수급권자수시설수급자-가구수시설수급자-수급권자수
날짜1.0000.0000.3480.2700.3730.3410.5720.4960.6770.3780.0000.0000.2240.221
읍면동0.0001.0000.5600.6390.5580.6790.4990.6100.4280.4960.1900.3700.6320.615
총 가구수0.3480.5601.0000.9550.9940.9660.9250.8440.7270.4240.1810.2760.2040.320
총 수급권자수0.2700.6390.9551.0000.9580.9830.8910.8670.6910.4990.0000.1690.2140.099
일반수급자-가구수0.3730.5580.9940.9581.0000.9680.9200.8100.7390.3860.2030.3200.3040.391
일반수급자-수급권자수0.3410.6790.9660.9830.9681.0000.8790.8300.8180.5970.0660.2460.3300.323
조건부수급자-가구수0.5720.4990.9250.8910.9200.8791.0000.9320.6350.2840.0000.0000.0000.247
조건부수급자-수급권자수0.4960.6100.8440.8670.8100.8300.9321.0000.5400.4500.0660.0000.2100.192
특례수급자-가구수0.6770.4280.7270.6910.7390.8180.6350.5401.0000.9090.0000.0000.3400.337
특례수급자-수급권자수0.3780.4960.4240.4990.3860.5970.2840.4500.9091.0000.0750.1940.2460.164
기타-가구수0.0000.1900.1810.0000.2030.0660.0000.0660.0000.0751.0000.7100.0000.000
기타-수급권자수0.0000.3700.2760.1690.3200.2460.0000.0000.0000.1940.7101.0000.0000.000
시설수급자-가구수0.2240.6320.2040.2140.3040.3300.0000.2100.3400.2460.0000.0001.0000.978
시설수급자-수급권자수0.2210.6150.3200.0990.3910.3230.2470.1920.3370.1640.0000.0000.9781.000
2023-12-12T12:24:57.727327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타-가구수기타-수급권자수읍면동
기타-가구수1.0000.9510.170
기타-수급권자수0.9511.0000.219
읍면동0.1700.2191.000
2023-12-12T12:24:57.869599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜총 가구수총 수급권자수일반수급자-가구수일반수급자-수급권자수조건부수급자-가구수조건부수급자-수급권자수특례수급자-가구수특례수급자-수급권자수시설수급자-가구수시설수급자-수급권자수읍면동기타-가구수기타-수급권자수
날짜1.0000.6870.4790.6870.4740.6350.4720.4670.1540.1690.0090.0000.0000.000
총 가구수0.6871.0000.9130.9960.9060.9030.8000.6050.4010.2650.1050.2680.1340.167
총 수급권자수0.4790.9131.0000.9070.9910.8350.8740.4880.4150.1860.1500.3280.0000.095
일반수급자-가구수0.6870.9960.9071.0000.9090.8800.7720.5890.3840.2280.0680.2660.1510.198
일반수급자-수급권자수0.4740.9060.9910.9091.0000.8000.8260.4490.3690.1510.1110.3610.0300.141
조건부수급자-가구수0.6350.9030.8350.8800.8001.0000.9140.4940.3160.1880.0330.2170.0000.000
조건부수급자-수급권자수0.4720.8000.8740.7720.8260.9141.0000.3910.3200.1520.1200.3030.0470.000
특례수급자-가구수0.4670.6050.4880.5890.4490.4940.3911.0000.8730.3070.1500.1900.0000.000
특례수급자-수급권자수0.1540.4010.4150.3840.3690.3160.3200.8731.0000.1970.1210.2280.0540.114
시설수급자-가구수0.1690.2650.1860.2280.1510.1880.1520.3070.1971.0000.8990.3510.0000.000
시설수급자-수급권자수0.0090.1050.1500.0680.1110.0330.1200.1500.1210.8991.0000.3300.0000.000
읍면동0.0000.2680.3280.2660.3610.2170.3030.1900.2280.3510.3301.0000.1700.219
기타-가구수0.0000.1340.0000.1510.0300.0000.0470.0000.0540.0000.0000.1701.0000.951
기타-수급권자수0.0000.1670.0950.1980.1410.0000.0000.0000.1140.0000.0000.2190.9511.000

Missing values

2023-12-12T12:24:51.373492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:24:51.671178image/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

날짜시군구읍면동총 가구수총 수급권자수일반수급자-가구수일반수급자-수급권자수조건부수급자-가구수조건부수급자-수급권자수특례수급자-가구수특례수급자-수급권자수기타-가구수기타-수급권자수시설수급자-가구수시설수급자-수급권자수데이터기준일자
02010남구이천동06620575037012000382023-08-31
12010남구봉덕1동0540048603501900002023-08-31
22010남구봉덕2동0583048605309000352023-08-31
32010남구봉덕3동09000761012106000122023-08-31
42010남구대명1동0666057507102000002023-08-31
52010남구대명2동0815070608601400092023-08-31
62010남구대명3동01074096408902000012023-08-31
72010남구대명4동08770747010102800012023-08-31
82010남구대명5동0557044909701000012023-08-31
92010남구대명6동056605200420300012023-08-31
날짜시군구읍면동총 가구수총 수급권자수일반수급자-가구수일반수급자-수급권자수조건부수급자-가구수조건부수급자-수급권자수특례수급자-가구수특례수급자-수급권자수기타-가구수기타-수급권자수시설수급자-가구수시설수급자-수급권자수데이터기준일자
1722023남구봉덕3동130018121114149912924937440020202023-08-31
1732023남구대명1동9141304808109794194111200112023-08-31
1742023남구대명2동847110973093685137313500112023-08-31
1752023남구대명3동108514119461181103184334300332023-08-31
1762023남구대명4동10671474948127091171222700662023-08-31
1772023남구대명5동408562357468428191300002023-08-31
1782023남구대명6동10591515918124113126191200112023-08-31
1792023남구대명9동1466196413001684150262131500332023-08-31
1802023남구대명10동7781017674850981596800002023-08-31
1812023남구대명11동9271230819104586159182200442023-08-31