Overview

Dataset statistics

Number of variables14
Number of observations45
Missing cells7
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory124.9 B

Variable types

Categorical4
Text1
Numeric8
DateTime1

Dataset

Description경기도 고양시_기초생활보장수급자현황에 대한 데이터로 시군구, 일반수급자수, 조건부수급자수, 특례수급자수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/3078473/fileData.do

Alerts

시도 has constant value ""Constant
데이터기준일 has constant value ""Constant
시군구 is highly overall correlated with 시설수급자 가구수 and 3 other fieldsHigh correlation
기타 수급권자수 is highly overall correlated with 일반수급자 가구수 and 9 other fieldsHigh correlation
기타 가구수 is highly overall correlated with 일반수급자 가구수 and 9 other fieldsHigh correlation
일반수급자 가구수 is highly overall correlated with 일반수급자 수급권자수 and 6 other fieldsHigh correlation
일반수급자 수급권자수 is highly overall correlated with 일반수급자 가구수 and 6 other fieldsHigh correlation
조건부수급자 가구수 is highly overall correlated with 일반수급자 가구수 and 6 other fieldsHigh correlation
조건부수급자 수급권자수 is highly overall correlated with 일반수급자 가구수 and 6 other fieldsHigh correlation
특례수급자 가구수 is highly overall correlated with 일반수급자 가구수 and 6 other fieldsHigh correlation
특례수급자 수급권자수 is highly overall correlated with 일반수급자 가구수 and 6 other fieldsHigh correlation
시설수급자 가구수 is highly overall correlated with 시설수급자 수급권자수 and 3 other fieldsHigh correlation
시설수급자 수급권자수 is highly overall correlated with 시설수급자 가구수 and 3 other fieldsHigh correlation
기타 가구수 is highly imbalanced (84.6%)Imbalance
기타 수급권자수 is highly imbalanced (84.6%)Imbalance
행정동명 has 1 (2.2%) missing valuesMissing
일반수급자 가구수 has 1 (2.2%) missing valuesMissing
일반수급자 수급권자수 has 1 (2.2%) missing valuesMissing
조건부수급자 가구수 has 1 (2.2%) missing valuesMissing
조건부수급자 수급권자수 has 1 (2.2%) missing valuesMissing
특례수급자 가구수 has 1 (2.2%) missing valuesMissing
특례수급자 수급권자수 has 1 (2.2%) missing valuesMissing
특례수급자 가구수 has 9 (20.0%) zerosZeros
특례수급자 수급권자수 has 9 (20.0%) zerosZeros
시설수급자 가구수 has 5 (11.1%) zerosZeros
시설수급자 수급권자수 has 5 (11.1%) zerosZeros

Reproduction

Analysis started2023-12-12 08:37:33.297297
Analysis finished2023-12-12 08:37:41.689257
Duration8.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
경기도
45 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 45
100.0%

Length

2023-12-12T17:37:41.769394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:37:41.905195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 45
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
고양시 덕양구
21 
고양시 일산동구
12 
고양시 일산서구
11 
고양시
 
1

Length

Max length8
Median length8
Mean length7.4222222
Min length3

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row고양시
2nd row고양시 덕양구
3rd row고양시 덕양구
4th row고양시 덕양구
5th row고양시 덕양구

Common Values

ValueCountFrequency (%)
고양시 덕양구 21
46.7%
고양시 일산동구 12
26.7%
고양시 일산서구 11
24.4%
고양시 1
 
2.2%

Length

2023-12-12T17:37:42.070454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:37:42.198049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 45
50.6%
덕양구 21
23.6%
일산동구 12
 
13.5%
일산서구 11
 
12.4%

행정동명
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size492.0 B
2023-12-12T17:37:42.431509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5909091
Min length3

Characters and Unicode

Total characters158
Distinct characters50
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

Unique44 ?
Unique (%)100.0%

Sample

1st row주교동
2nd row원신동
3rd row흥도동
4th row성사1동
5th row성사2동
ValueCountFrequency (%)
주교동 1
 
2.3%
원신동 1
 
2.3%
정발산동 1
 
2.3%
풍산동 1
 
2.3%
백석1동 1
 
2.3%
백석2동 1
 
2.3%
마두1동 1
 
2.3%
마두2동 1
 
2.3%
장항1동 1
 
2.3%
장항2동 1
 
2.3%
Other values (34) 34
77.3%
2023-12-12T17:37:42.824117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 44
27.8%
1 11
 
7.0%
2 11
 
7.0%
산 8
 
5.1%
행 5
 
3.2%
신 5
 
3.2%
화 4
 
2.5%
주 4
 
2.5%
사 3
 
1.9%
일 3
 
1.9%
Other values (40) 60
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
84.2%
Decimal Number 25
 
15.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
화 4
 
3.0%
주 4
 
3.0%
사 3
 
2.3%
일 3
 
2.3%
송 3
 
2.3%
정 3
 
2.3%
Other values (36) 51
38.3%
Decimal Number
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
84.2%
Common 25
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
화 4
 
3.0%
주 4
 
3.0%
사 3
 
2.3%
일 3
 
2.3%
송 3
 
2.3%
정 3
 
2.3%
Other values (36) 51
38.3%
Common
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
84.2%
ASCII 25
 
15.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
화 4
 
3.0%
주 4
 
3.0%
사 3
 
2.3%
일 3
 
2.3%
송 3
 
2.3%
정 3
 
2.3%
Other values (36) 51
38.3%
ASCII
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%

일반수급자 가구수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean508.86364
Minimum49
Maximum1458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:42.962243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile126.3
Q1193.75
median422.5
Q3687.75
95-th percentile1210
Maximum1458
Range1409
Interquartile range (IQR)494

Descriptive statistics

Standard deviation357.01427
Coefficient of variation (CV)0.70159124
Kurtosis0.32471367
Mean508.86364
Median Absolute Deviation (MAD)246
Skewness0.93906332
Sum22390
Variance127459.19
MonotonicityNot monotonic
2023-12-12T17:37:43.108897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
545 1
 
2.2%
683 1
 
2.2%
687 1
 
2.2%
334 1
 
2.2%
1091 1
 
2.2%
193 1
 
2.2%
126 1
 
2.2%
49 1
 
2.2%
552 1
 
2.2%
454 1
 
2.2%
Other values (34) 34
75.6%
ValueCountFrequency (%)
49 1
2.2%
104 1
2.2%
126 1
2.2%
128 1
2.2%
143 1
2.2%
159 1
2.2%
162 1
2.2%
173 1
2.2%
176 1
2.2%
177 1
2.2%
ValueCountFrequency (%)
1458 1
2.2%
1387 1
2.2%
1231 1
2.2%
1091 1
2.2%
948 1
2.2%
889 1
2.2%
887 1
2.2%
866 1
2.2%
859 1
2.2%
783 1
2.2%

일반수급자 수급권자수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean696.15909
Minimum68
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:43.273330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile162.2
Q1271.75
median569
Q3983.25
95-th percentile1682.7
Maximum2100
Range2032
Interquartile range (IQR)711.5

Descriptive statistics

Standard deviation488.76165
Coefficient of variation (CV)0.70208327
Kurtosis0.79169701
Mean696.15909
Median Absolute Deviation (MAD)327.5
Skewness1.0490403
Sum30631
Variance238887.95
MonotonicityNot monotonic
2023-12-12T17:37:43.441712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
740 1
 
2.2%
1093 1
 
2.2%
1035 1
 
2.2%
488 1
 
2.2%
1375 1
 
2.2%
274 1
 
2.2%
155 1
 
2.2%
68 1
 
2.2%
681 1
 
2.2%
632 1
 
2.2%
Other values (34) 34
75.6%
ValueCountFrequency (%)
68 1
2.2%
152 1
2.2%
155 1
2.2%
203 1
2.2%
210 1
2.2%
224 1
2.2%
234 1
2.2%
239 1
2.2%
244 1
2.2%
261 1
2.2%
ValueCountFrequency (%)
2100 1
2.2%
1902 1
2.2%
1737 1
2.2%
1375 1
2.2%
1353 1
2.2%
1143 1
2.2%
1124 1
2.2%
1114 1
2.2%
1103 1
2.2%
1093 1
2.2%

조건부수급자 가구수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)86.4%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean54.431818
Minimum4
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:43.579965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.45
Q119.75
median43
Q375.25
95-th percentile120.55
Maximum207
Range203
Interquartile range (IQR)55.5

Descriptive statistics

Standard deviation43.471668
Coefficient of variation (CV)0.79864443
Kurtosis2.2520207
Mean54.431818
Median Absolute Deviation (MAD)27.5
Skewness1.2959335
Sum2395
Variance1889.7859
MonotonicityNot monotonic
2023-12-12T17:37:43.724856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
20 2
 
4.4%
15 2
 
4.4%
70 2
 
4.4%
34 2
 
4.4%
22 2
 
4.4%
86 2
 
4.4%
67 1
 
2.2%
68 1
 
2.2%
12 1
 
2.2%
77 1
 
2.2%
Other values (28) 28
62.2%
ValueCountFrequency (%)
4 1
2.2%
6 1
2.2%
7 1
2.2%
10 1
2.2%
11 1
2.2%
12 1
2.2%
13 1
2.2%
15 2
4.4%
16 1
2.2%
19 1
2.2%
ValueCountFrequency (%)
207 1
2.2%
150 1
2.2%
121 1
2.2%
118 1
2.2%
113 1
2.2%
103 1
2.2%
97 1
2.2%
86 2
4.4%
77 1
2.2%
76 1
2.2%

조건부수급자 수급권자수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)88.6%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean88.522727
Minimum7
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:43.867289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13.3
Q132.5
median79.5
Q3122.25
95-th percentile187.7
Maximum314
Range307
Interquartile range (IQR)89.75

Descriptive statistics

Standard deviation67.197207
Coefficient of variation (CV)0.75909553
Kurtosis1.5748339
Mean88.522727
Median Absolute Deviation (MAD)47.5
Skewness1.1310524
Sum3895
Variance4515.4646
MonotonicityNot monotonic
2023-12-12T17:37:44.016303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
31 3
 
6.7%
137 2
 
4.4%
110 2
 
4.4%
96 2
 
4.4%
63 1
 
2.2%
117 1
 
2.2%
59 1
 
2.2%
186 1
 
2.2%
34 1
 
2.2%
16 1
 
2.2%
Other values (29) 29
64.4%
ValueCountFrequency (%)
7 1
 
2.2%
11 1
 
2.2%
13 1
 
2.2%
15 1
 
2.2%
16 1
 
2.2%
22 1
 
2.2%
23 1
 
2.2%
30 1
 
2.2%
31 3
6.7%
33 1
 
2.2%
ValueCountFrequency (%)
314 1
2.2%
231 1
2.2%
188 1
2.2%
186 1
2.2%
183 1
2.2%
169 1
2.2%
164 1
2.2%
137 2
4.4%
135 1
2.2%
129 1
2.2%

특례수급자 가구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)38.6%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean5.2954545
Minimum0
Maximum23
Zeros9
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:44.141072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q38.25
95-th percentile14.85
Maximum23
Range23
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation5.4881672
Coefficient of variation (CV)1.0363921
Kurtosis1.3677478
Mean5.2954545
Median Absolute Deviation (MAD)3.5
Skewness1.2218035
Sum233
Variance30.119979
MonotonicityNot monotonic
2023-12-12T17:37:44.266680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9
20.0%
1 7
15.6%
9 3
 
6.7%
2 3
 
6.7%
8 3
 
6.7%
3 3
 
6.7%
6 3
 
6.7%
11 2
 
4.4%
7 2
 
4.4%
4 2
 
4.4%
Other values (7) 7
15.6%
ValueCountFrequency (%)
0 9
20.0%
1 7
15.6%
2 3
 
6.7%
3 3
 
6.7%
4 2
 
4.4%
5 1
 
2.2%
6 3
 
6.7%
7 2
 
4.4%
8 3
 
6.7%
9 3
 
6.7%
ValueCountFrequency (%)
23 1
 
2.2%
18 1
 
2.2%
15 1
 
2.2%
14 1
 
2.2%
13 1
 
2.2%
11 2
4.4%
10 1
 
2.2%
9 3
6.7%
8 3
6.7%
7 2
4.4%

특례수급자 수급권자수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)38.6%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean6.3409091
Minimum0
Maximum28
Zeros9
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:44.367693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q311
95-th percentile15.85
Maximum28
Range28
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7958372
Coefficient of variation (CV)1.0717449
Kurtosis1.7109054
Mean6.3409091
Median Absolute Deviation (MAD)3.5
Skewness1.3165517
Sum279
Variance46.183404
MonotonicityNot monotonic
2023-12-12T17:37:44.468379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9
20.0%
2 5
11.1%
1 5
11.1%
11 3
 
6.7%
15 3
 
6.7%
3 3
 
6.7%
8 2
 
4.4%
12 2
 
4.4%
9 2
 
4.4%
6 2
 
4.4%
Other values (7) 8
17.8%
ValueCountFrequency (%)
0 9
20.0%
1 5
11.1%
2 5
11.1%
3 3
 
6.7%
4 2
 
4.4%
6 2
 
4.4%
7 1
 
2.2%
8 2
 
4.4%
9 2
 
4.4%
10 1
 
2.2%
ValueCountFrequency (%)
28 1
 
2.2%
25 1
 
2.2%
16 1
 
2.2%
15 3
6.7%
13 1
 
2.2%
12 2
4.4%
11 3
6.7%
10 1
 
2.2%
9 2
4.4%
8 2
4.4%

기타 가구수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
44 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0666667
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44
97.8%
<NA> 1
 
2.2%

Length

2023-12-12T17:37:44.581575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:37:44.689054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44
97.8%
na 1
 
2.2%

기타 수급권자수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
44 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0666667
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44
97.8%
<NA> 1
 
2.2%

Length

2023-12-12T17:37:44.788243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:37:44.903747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44
97.8%
na 1
 
2.2%

시설수급자 가구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.733333
Minimum0
Maximum1140
Zeros5
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:45.016612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q311
95-th percentile123.4
Maximum1140
Range1140
Interquartile range (IQR)9

Descriptive statistics

Standard deviation170.48466
Coefficient of variation (CV)4.4014972
Kurtosis42.146863
Mean38.733333
Median Absolute Deviation (MAD)3
Skewness6.4124417
Sum1743
Variance29065.018
MonotonicityNot monotonic
2023-12-12T17:37:45.136906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 6
13.3%
1 6
13.3%
0 5
11.1%
3 4
 
8.9%
10 3
 
6.7%
6 3
 
6.7%
4 2
 
4.4%
5 2
 
4.4%
15 2
 
4.4%
7 2
 
4.4%
Other values (10) 10
22.2%
ValueCountFrequency (%)
0 5
11.1%
1 6
13.3%
2 6
13.3%
3 4
8.9%
4 2
 
4.4%
5 2
 
4.4%
6 3
6.7%
7 2
 
4.4%
10 3
6.7%
11 1
 
2.2%
ValueCountFrequency (%)
1140 1
2.2%
138 1
2.2%
135 1
2.2%
77 1
2.2%
32 1
2.2%
25 1
2.2%
19 1
2.2%
15 2
4.4%
14 1
2.2%
12 1
2.2%

시설수급자 수급권자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.733333
Minimum0
Maximum1140
Zeros5
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T17:37:45.257424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q311
95-th percentile123.4
Maximum1140
Range1140
Interquartile range (IQR)9

Descriptive statistics

Standard deviation170.48466
Coefficient of variation (CV)4.4014972
Kurtosis42.146863
Mean38.733333
Median Absolute Deviation (MAD)3
Skewness6.4124417
Sum1743
Variance29065.018
MonotonicityNot monotonic
2023-12-12T17:37:45.406804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 6
13.3%
1 6
13.3%
0 5
11.1%
3 4
 
8.9%
10 3
 
6.7%
6 3
 
6.7%
4 2
 
4.4%
5 2
 
4.4%
15 2
 
4.4%
7 2
 
4.4%
Other values (10) 10
22.2%
ValueCountFrequency (%)
0 5
11.1%
1 6
13.3%
2 6
13.3%
3 4
8.9%
4 2
 
4.4%
5 2
 
4.4%
6 3
6.7%
7 2
 
4.4%
10 3
6.7%
11 1
 
2.2%
ValueCountFrequency (%)
1140 1
2.2%
138 1
2.2%
135 1
2.2%
77 1
2.2%
32 1
2.2%
25 1
2.2%
19 1
2.2%
15 2
4.4%
14 1
2.2%
12 1
2.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
Minimum2023-07-20 00:00:00
Maximum2023-07-20 00:00:00
2023-12-12T17:37:45.515985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:45.625096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T17:37:39.805357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:33.722488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.717822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.506308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.228678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.986307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.925375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.934601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.935213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:33.814008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.857974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.592283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.312576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.077812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.045376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.047339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:40.045058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.179106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.965396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.704325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.405701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.180304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.155546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.180995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:40.470321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.255224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.052480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.796829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.497501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.321295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.315173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.308629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:40.576782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.332349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.140330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.878838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.603216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.424796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.442062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.415149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:40.692153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.417152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.224587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.959930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.689493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.519479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.557082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.505755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:40.800646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.540615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.322446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.049261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.786716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.605788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.659266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.600769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:40.900700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:34.630035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:35.417132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.146715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:36.875226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:37.723695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:38.772695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:37:39.689753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:37:45.732826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구행정동명일반수급자 가구수일반수급자 수급권자수조건부수급자 가구수조건부수급자 수급권자수특례수급자 가구수특례수급자 수급권자수시설수급자 가구수시설수급자 수급권자수
시군구1.0001.0000.0000.0000.2210.0000.3220.3760.6690.669
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
일반수급자 가구수0.0001.0001.0000.9610.8490.8060.7490.7710.0000.000
일반수급자 수급권자수0.0001.0000.9611.0000.7940.8060.8830.7260.0000.000
조건부수급자 가구수0.2211.0000.8490.7941.0000.9780.7850.9180.0000.000
조건부수급자 수급권자수0.0001.0000.8060.8060.9781.0000.7750.8870.0000.000
특례수급자 가구수0.3221.0000.7490.8830.7850.7751.0000.9540.0000.000
특례수급자 수급권자수0.3761.0000.7710.7260.9180.8870.9541.0000.0000.000
시설수급자 가구수0.6691.0000.0000.0000.0000.0000.0000.0001.0001.000
시설수급자 수급권자수0.6691.0000.0000.0000.0000.0000.0000.0001.0001.000
2023-12-12T17:37:45.884204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구기타 수급권자수기타 가구수
시군구1.0001.0001.000
기타 수급권자수1.0001.0001.000
기타 가구수1.0001.0001.000
2023-12-12T17:37:46.019526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반수급자 가구수일반수급자 수급권자수조건부수급자 가구수조건부수급자 수급권자수특례수급자 가구수특례수급자 수급권자수시설수급자 가구수시설수급자 수급권자수시군구기타 가구수기타 수급권자수
일반수급자 가구수1.0000.9920.9230.9380.8210.8080.1490.1490.0001.0001.000
일반수급자 수급권자수0.9921.0000.9110.9420.8300.8280.1740.1740.0001.0001.000
조건부수급자 가구수0.9230.9111.0000.9770.7890.7620.1690.1690.1191.0001.000
조건부수급자 수급권자수0.9380.9420.9771.0000.8100.7980.2130.2130.0001.0001.000
특례수급자 가구수0.8210.8300.7890.8101.0000.9850.2000.2000.1221.0001.000
특례수급자 수급권자수0.8080.8280.7620.7980.9851.0000.1960.1960.2351.0001.000
시설수급자 가구수0.1490.1740.1690.2130.2000.1961.0001.0000.6891.0001.000
시설수급자 수급권자수0.1490.1740.1690.2130.2000.1961.0001.0000.6891.0001.000
시군구0.0000.0000.1190.0000.1220.2350.6890.6891.0001.0001.000
기타 가구수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기타 수급권자수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T17:37:41.070599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:37:41.321609image/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.
2023-12-12T17:37:41.560794image/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

시도시군구행정동명일반수급자 가구수일반수급자 수급권자수조건부수급자 가구수조건부수급자 수급권자수특례수급자 가구수특례수급자 수급권자수기타 가구수기타 수급권자수시설수급자 가구수시설수급자 수급권자수데이터기준일
0경기도고양시<NA><NA><NA><NA><NA><NA><NA><NA><NA>114011402023-07-20
1경기도고양시 덕양구주교동545740761139110010102023-07-20
2경기도고양시 덕양구원신동887135370137131500222023-07-20
3경기도고양시 덕양구흥도동65386286129111100552023-07-20
4경기도고양시 덕양구성사1동69093897135990011112023-07-20
5경기도고양시 덕양구성사2동20326510111100112023-07-20
6경기도고양시 덕양구효자동9481114701106800112023-07-20
7경기도고양시 덕양구삼송1동21828019352200002023-07-20
8경기도고양시 덕양구삼송2동14582100150231232800112023-07-20
9경기도고양시 덕양구창릉동1942751633000015152023-07-20
시도시군구행정동명일반수급자 가구수일반수급자 수급권자수조건부수급자 가구수조건부수급자 수급권자수특례수급자 가구수특례수급자 수급권자수기타 가구수기타 수급권자수시설수급자 가구수시설수급자 수급권자수데이터기준일
35경기도고양시 일산서구일산2동56270577120151600442023-07-20
36경기도고양시 일산서구일산3동12820315300000002023-07-20
37경기도고양시 일산서구탄현1동3424783454330014142023-07-20
38경기도고양시 일산서구탄현2동162234122222001351352023-07-20
39경기도고양시 일산서구주엽1동17323922310000112023-07-20
40경기도고양시 일산서구주엽2동88911246810481100332023-07-20
41경기도고양시 일산서구대화동637905671101100332023-07-20
42경기도고양시 일산서구송포동10415211232200332023-07-20
43경기도고양시 일산서구덕이동29341620370000222023-07-20
44경기도고양시 일산서구가좌동1592616131200662023-07-20