Overview

Dataset statistics

Number of variables8
Number of observations39
Missing cells2
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory73.3 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description사회조사 지역사회교육의질(아주 좋은 편, 좋은 편, 잘모름 등) 제공해당 데이터는 기관에서 더이상 생성할 수 없는 파일입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15051300/fileData.do

Alerts

좋은 편 is highly overall correlated with 그저 그런 편High correlation
그저 그런 편 is highly overall correlated with 좋은 편 and 1 other fieldsHigh correlation
좋지 못함 is highly overall correlated with 아주 좋지 못함High correlation
아주 좋지 못함 is highly overall correlated with 좋지 못함High correlation
잘 모름 is highly overall correlated with 그저 그런 편High correlation
아주 좋은 편 has 1 (2.6%) missing valuesMissing
아주 좋지 못함 has 1 (2.6%) missing valuesMissing
상세구분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 15:44:28.377447
Analysis finished2024-03-14 15:44:38.175009
Duration9.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct9
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size440.0 B
학력
7 
연령
6 
취업상태
5 
결혼
5 
주택형태
5 
Other values (4)
11 

Length

Max length5
Median length2
Mean length2.9230769
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시군
2nd row시군
3rd row성별
4th row성별
5th row연령

Common Values

ValueCountFrequency (%)
학력 7
17.9%
연령 6
15.4%
취업상태 5
12.8%
결혼 5
12.8%
주택형태 5
12.8%
세대구분 5
12.8%
시군 2
 
5.1%
성별 2
 
5.1%
조사구구분 2
 
5.1%

Length

2024-03-15T00:44:38.490586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:44:38.893806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
학력 7
17.9%
연령 6
15.4%
취업상태 5
12.8%
결혼 5
12.8%
주택형태 5
12.8%
세대구분 5
12.8%
시군 2
 
5.1%
성별 2
 
5.1%
조사구구분 2
 
5.1%

상세구분
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size440.0 B
2024-03-15T00:44:40.137769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.025641
Min length2

Characters and Unicode

Total characters157
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row시부
2nd row군부
3rd row남자
4th row여자
5th row15~19세
ValueCountFrequency (%)
시부 1
 
2.4%
아파트 1
 
2.4%
배우자 1
 
2.4%
있음 1
 
2.4%
사별 1
 
2.4%
이혼 1
 
2.4%
별거 1
 
2.4%
가구주 1
 
2.4%
가구원 1
 
2.4%
기능/기타 1
 
2.4%
Other values (31) 31
75.6%
2024-03-15T00:44:41.966100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 10
 
6.4%
구 8
 
5.1%
가 8
 
5.1%
대 7
 
4.5%
학 6
 
3.8%
~ 5
 
3.2%
9 5
 
3.2%
0 5
 
3.2%
자 4
 
2.5%
1 4
 
2.5%
Other values (59) 95
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
77.1%
Decimal Number 26
 
16.6%
Math Symbol 5
 
3.2%
Other Punctuation 3
 
1.9%
Space Separator 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 10
 
8.3%
구 8
 
6.6%
가 8
 
6.6%
대 7
 
5.8%
학 6
 
5.0%
자 4
 
3.3%
주 4
 
3.3%
교 4
 
3.3%
사 3
 
2.5%
비 3
 
2.5%
Other values (48) 64
52.9%
Decimal Number
ValueCountFrequency (%)
9 5
19.2%
0 5
19.2%
1 4
15.4%
3 3
11.5%
5 3
11.5%
2 3
11.5%
4 2
 
7.7%
6 1
 
3.8%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
77.1%
Common 36
 
22.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 10
 
8.3%
구 8
 
6.6%
가 8
 
6.6%
대 7
 
5.8%
학 6
 
5.0%
자 4
 
3.3%
주 4
 
3.3%
교 4
 
3.3%
사 3
 
2.5%
비 3
 
2.5%
Other values (48) 64
52.9%
Common
ValueCountFrequency (%)
~ 5
13.9%
9 5
13.9%
0 5
13.9%
1 4
11.1%
/ 3
8.3%
3 3
8.3%
5 3
8.3%
2 3
8.3%
2
 
5.6%
4 2
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
77.1%
ASCII 36
 
22.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 10
 
8.3%
구 8
 
6.6%
가 8
 
6.6%
대 7
 
5.8%
학 6
 
5.0%
자 4
 
3.3%
주 4
 
3.3%
교 4
 
3.3%
사 3
 
2.5%
비 3
 
2.5%
Other values (48) 64
52.9%
ASCII
ValueCountFrequency (%)
~ 5
13.9%
9 5
13.9%
0 5
13.9%
1 4
11.1%
/ 3
8.3%
3 3
8.3%
5 3
8.3%
2 3
8.3%
2
 
5.6%
4 2
 
5.6%

아주 좋은 편
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)42.1%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1.7605263
Minimum0.9
Maximum3.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T00:44:42.618858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile0.985
Q11.425
median1.75
Q32.075
95-th percentile2.375
Maximum3.1
Range2.2
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.48964382
Coefficient of variation (CV)0.27812354
Kurtosis0.52108618
Mean1.7605263
Median Absolute Deviation (MAD)0.35
Skewness0.37031711
Sum66.9
Variance0.23975107
MonotonicityNot monotonic
2024-03-15T00:44:43.045583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.7 4
10.3%
2.0 4
10.3%
1.6 4
10.3%
1.8 4
10.3%
2.3 3
7.7%
1.4 3
7.7%
2.1 3
7.7%
1.2 2
 
5.1%
2.2 2
 
5.1%
1.1 2
 
5.1%
Other values (6) 7
17.9%
ValueCountFrequency (%)
0.9 2
5.1%
1.0 1
 
2.6%
1.1 2
5.1%
1.2 2
5.1%
1.4 3
7.7%
1.5 1
 
2.6%
1.6 4
10.3%
1.7 4
10.3%
1.8 4
10.3%
1.9 1
 
2.6%
ValueCountFrequency (%)
3.1 1
 
2.6%
2.8 1
 
2.6%
2.3 3
7.7%
2.2 2
5.1%
2.1 3
7.7%
2.0 4
10.3%
1.9 1
 
2.6%
1.8 4
10.3%
1.7 4
10.3%
1.6 4
10.3%

좋은 편
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.815385
Minimum13.1
Maximum32.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T00:44:43.597150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.1
5-th percentile17.21
Q123.1
median25.8
Q326.8
95-th percentile28.56
Maximum32.6
Range19.5
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation3.7583268
Coefficient of variation (CV)0.15145148
Kurtosis2.7105217
Mean24.815385
Median Absolute Deviation (MAD)1.2
Skewness-1.3696229
Sum967.8
Variance14.12502
MonotonicityNot monotonic
2024-03-15T00:44:44.029204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
25.4 3
 
7.7%
26.7 3
 
7.7%
26.5 2
 
5.1%
26.2 2
 
5.1%
27.6 2
 
5.1%
26.9 2
 
5.1%
25.8 1
 
2.6%
17.5 1
 
2.6%
13.1 1
 
2.6%
25.1 1
 
2.6%
Other values (21) 21
53.8%
ValueCountFrequency (%)
13.1 1
2.6%
14.6 1
2.6%
17.5 1
2.6%
19.3 1
2.6%
20.4 1
2.6%
22.2 1
2.6%
22.5 1
2.6%
22.6 1
2.6%
22.8 1
2.6%
22.9 1
2.6%
ValueCountFrequency (%)
32.6 1
 
2.6%
29.1 1
 
2.6%
28.5 1
 
2.6%
27.9 1
 
2.6%
27.6 2
5.1%
27.2 1
 
2.6%
27.0 1
 
2.6%
26.9 2
5.1%
26.7 3
7.7%
26.5 2
5.1%

그저 그런 편
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.107692
Minimum32.7
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T00:44:44.466312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.7
5-th percentile36.27
Q143.5
median48.2
Q351.35
95-th percentile53.69
Maximum58.5
Range25.8
Interquartile range (IQR)7.85

Descriptive statistics

Standard deviation5.8871128
Coefficient of variation (CV)0.12497137
Kurtosis0.059279407
Mean47.107692
Median Absolute Deviation (MAD)3.5
Skewness-0.67503627
Sum1837.2
Variance34.658097
MonotonicityNot monotonic
2024-03-15T00:44:44.722672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
48.2 2
 
5.1%
51.2 2
 
5.1%
43.5 2
 
5.1%
45.1 2
 
5.1%
49.3 1
 
2.6%
40.7 1
 
2.6%
46.5 1
 
2.6%
36.5 1
 
2.6%
51.0 1
 
2.6%
54.5 1
 
2.6%
Other values (25) 25
64.1%
ValueCountFrequency (%)
32.7 1
2.6%
34.2 1
2.6%
36.5 1
2.6%
37.7 1
2.6%
38.7 1
2.6%
40.7 1
2.6%
40.9 1
2.6%
42.8 1
2.6%
43.4 1
2.6%
43.5 2
5.1%
ValueCountFrequency (%)
58.5 1
2.6%
54.5 1
2.6%
53.6 1
2.6%
53.3 1
2.6%
53.1 1
2.6%
52.7 1
2.6%
52.2 1
2.6%
51.9 1
2.6%
51.7 1
2.6%
51.4 1
2.6%

좋지 못함
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.289744
Minimum6.3
Maximum18.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T00:44:44.935071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.3
5-th percentile8.5
Q111.2
median12.3
Q313.45
95-th percentile16.15
Maximum18.1
Range11.8
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.2764001
Coefficient of variation (CV)0.18522763
Kurtosis1.4319959
Mean12.289744
Median Absolute Deviation (MAD)1.2
Skewness0.098960247
Sum479.3
Variance5.1819973
MonotonicityNot monotonic
2024-03-15T00:44:45.165670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
13.4 2
 
5.1%
12.1 2
 
5.1%
11.0 2
 
5.1%
13.2 2
 
5.1%
11.6 2
 
5.1%
13.7 2
 
5.1%
12.3 2
 
5.1%
8.5 2
 
5.1%
12.5 2
 
5.1%
13.5 2
 
5.1%
Other values (17) 19
48.7%
ValueCountFrequency (%)
6.3 1
2.6%
8.5 2
5.1%
8.7 1
2.6%
10.2 1
2.6%
10.7 1
2.6%
10.8 1
2.6%
10.9 1
2.6%
11.0 2
5.1%
11.4 2
5.1%
11.5 1
2.6%
ValueCountFrequency (%)
18.1 1
2.6%
17.5 1
2.6%
16.0 1
2.6%
15.0 1
2.6%
14.7 1
2.6%
13.7 2
5.1%
13.6 1
2.6%
13.5 2
5.1%
13.4 2
5.1%
13.2 2
5.1%

아주 좋지 못함
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)55.3%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean3.8342105
Minimum1.5
Maximum6.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T00:44:45.443536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.37
Q13.425
median3.7
Q34.2
95-th percentile5.96
Maximum6.9
Range5.4
Interquartile range (IQR)0.775

Descriptive statistics

Standard deviation1.0568378
Coefficient of variation (CV)0.27563374
Kurtosis1.7687217
Mean3.8342105
Median Absolute Deviation (MAD)0.45
Skewness0.6844635
Sum145.7
Variance1.1169061
MonotonicityNot monotonic
2024-03-15T00:44:45.821371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.7 5
12.8%
3.5 4
 
10.3%
4.2 4
 
10.3%
3.3 2
 
5.1%
4.1 2
 
5.1%
2.6 2
 
5.1%
4.0 2
 
5.1%
4.4 2
 
5.1%
4.6 2
 
5.1%
3.9 2
 
5.1%
Other values (11) 11
28.2%
ValueCountFrequency (%)
1.5 1
 
2.6%
2.2 1
 
2.6%
2.4 1
 
2.6%
2.5 1
 
2.6%
2.6 2
5.1%
2.8 1
 
2.6%
3.3 2
5.1%
3.4 1
 
2.6%
3.5 4
10.3%
3.6 1
 
2.6%
ValueCountFrequency (%)
6.9 1
 
2.6%
6.3 1
 
2.6%
5.9 1
 
2.6%
5.1 1
 
2.6%
4.6 2
5.1%
4.4 2
5.1%
4.2 4
10.3%
4.1 2
5.1%
4.0 2
5.1%
3.9 2
5.1%

잘 모름
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.330769
Minimum2.7
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T00:44:46.187694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.9
Q16.7
median8.6
Q312.15
95-th percentile21.91
Maximum25.9
Range23.2
Interquartile range (IQR)5.45

Descriptive statistics

Standard deviation5.5835357
Coefficient of variation (CV)0.54047627
Kurtosis1.1767056
Mean10.330769
Median Absolute Deviation (MAD)2.5
Skewness1.1991282
Sum402.9
Variance31.17587
MonotonicityNot monotonic
2024-03-15T00:44:46.501245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8.6 3
 
7.7%
11.1 3
 
7.7%
7.7 2
 
5.1%
6.2 2
 
5.1%
6.7 2
 
5.1%
15.7 1
 
2.6%
12.4 1
 
2.6%
7.0 1
 
2.6%
8.0 1
 
2.6%
24.7 1
 
2.6%
Other values (22) 22
56.4%
ValueCountFrequency (%)
2.7 1
2.6%
3.0 1
2.6%
4.0 1
2.6%
4.2 1
2.6%
5.0 1
2.6%
5.3 1
2.6%
5.9 1
2.6%
6.2 2
5.1%
6.7 2
5.1%
7.0 1
2.6%
ValueCountFrequency (%)
25.9 1
2.6%
24.7 1
2.6%
21.6 1
2.6%
18.0 1
2.6%
17.4 1
2.6%
17.1 1
2.6%
15.7 1
2.6%
15.4 1
2.6%
13.2 1
2.6%
12.4 1
2.6%

Interactions

2024-03-15T00:44:35.552603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:28.725001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:30.126446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:31.672336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:32.638648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:34.075141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:35.784348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:28.960011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:30.380122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:31.812804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:32.795103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:34.317589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:36.050472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:29.221719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:30.652194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:31.978359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:33.114836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:34.584611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:36.284905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:29.416860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:30.907067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:32.128629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:33.361598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:34.821048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:36.594634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:29.650946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:31.167960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:32.273001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:33.589977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:35.072392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:36.846786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:29.893429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:31.424608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:32.499288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:33.838883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:44:35.314588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:44:46.665486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상세구분아주 좋은 편좋은 편그저 그런 편좋지 못함아주 좋지 못함잘 모름
구분1.0001.0000.4840.0000.0000.0000.0000.000
상세구분1.0001.0001.0001.0001.0001.0001.0001.000
아주 좋은 편0.4841.0001.0000.8530.0000.3890.7580.000
좋은 편0.0001.0000.8531.0000.4010.6680.8480.536
그저 그런 편0.0001.0000.0000.4011.0000.7070.5870.658
좋지 못함0.0001.0000.3890.6680.7071.0000.7740.795
아주 좋지 못함0.0001.0000.7580.8480.5870.7741.0000.510
잘 모름0.0001.0000.0000.5360.6580.7950.5101.000
2024-03-15T00:44:46.987344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아주 좋은 편좋은 편그저 그런 편좋지 못함아주 좋지 못함잘 모름구분
아주 좋은 편1.0000.401-0.3760.1030.247-0.0380.191
좋은 편0.4011.000-0.696-0.298-0.2280.1780.000
그저 그런 편-0.376-0.6961.0000.168-0.050-0.6510.000
좋지 못함0.103-0.2980.1681.0000.502-0.4490.000
아주 좋지 못함0.247-0.228-0.0500.5021.000-0.0550.000
잘 모름-0.0380.178-0.651-0.449-0.0551.0000.000
구분0.1910.0000.0000.0000.0000.0001.000

Missing values

2024-03-15T00:44:37.207995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:44:37.718152image/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.
2024-03-15T00:44:38.028601image/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

구분상세구분아주 좋은 편좋은 편그저 그런 편좋지 못함아주 좋지 못함잘 모름
0시군시부1.725.649.311.53.38.6
1시군군부2.326.734.215.06.315.4
2성별남자2.025.448.212.94.17.5
3성별여자1.726.145.311.43.511.9
4연령15~19세0.924.849.416.05.93.0
5연령20~29세1.122.852.713.52.27.7
6연령30~39세1.622.651.412.54.27.7
7연령40~49세1.826.251.712.14.24.0
8연령50~59세2.127.647.412.13.77.1
9연령60세이상2.127.238.710.83.717.4
구분상세구분아주 좋은 편좋은 편그저 그런 편좋지 못함아주 좋지 못함잘 모름
29주택형태단독주택2.325.840.713.74.213.2
30주택형태아파트1.426.351.211.03.56.7
31주택형태연립주택1.414.658.511.74.29.6
32주택형태다세대주택1.432.646.06.32.611.1
33주택형태기타3.119.345.814.76.910.2
34세대구분1인가구2.027.637.78.72.421.6
35세대구분1세대가구1.926.743.412.34.611.1
36세대구분2세대가구1.825.450.312.63.76.2
37세대구분3세대가구1.222.249.613.64.09.4
38세대구분비혈연가구<NA>20.451.310.2<NA>18.0