Overview

Dataset statistics

Number of variables8
Number of observations106
Missing cells14
Missing cells (%)1.7%
Duplicate rows1
Duplicate rows (%)0.9%
Total size in memory7.4 KiB
Average record size in memory71.2 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description강릉, 경주, 부산, 여수, 전주 5개 지자체 관광지에 방문한 외지인을 대상으로 신한카드 라이프스테이지(가족구성)_비율 데이터 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15108405/fileData.do

Alerts

Dataset has 1 (0.9%) duplicate rowsDuplicates
싱글 is highly overall correlated with 청소년자녀가족 and 2 other fieldsHigh correlation
청소년자녀가족 is highly overall correlated with 싱글High correlation
성인자녀가족 is highly overall correlated with 싱글High correlation
실버 is highly overall correlated with 싱글High correlation
관광지명 has 2 (1.9%) missing valuesMissing
싱글 has 2 (1.9%) missing valuesMissing
신혼 has 2 (1.9%) missing valuesMissing
영유아,어린이자녀가족 has 2 (1.9%) missing valuesMissing
청소년자녀가족 has 2 (1.9%) missing valuesMissing
성인자녀가족 has 2 (1.9%) missing valuesMissing
실버 has 2 (1.9%) missing valuesMissing
신혼 has 8 (7.5%) zerosZeros
영유아,어린이자녀가족 has 4 (3.8%) zerosZeros
성인자녀가족 has 2 (1.9%) zerosZeros
실버 has 2 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 06:41:05.769081
Analysis finished2023-12-12 06:41:10.283600
Duration4.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지자체명
Categorical

Distinct6
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size980.0 B
부산시
32 
여수시
25 
경주시
16 
전주시
16 
강릉시
15 

Length

Max length4
Median length3
Mean length3.0188679
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산시 32
30.2%
여수시 25
23.6%
경주시 16
15.1%
전주시 16
15.1%
강릉시 15
14.2%
<NA> 2
 
1.9%

Length

2023-12-12T15:41:10.377789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:41:10.867678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산시 32
30.2%
여수시 25
23.6%
경주시 16
15.1%
전주시 16
15.1%
강릉시 15
14.2%
na 2
 
1.9%

관광지명
Text

MISSING 

Distinct100
Distinct (%)96.2%
Missing2
Missing (%)1.9%
Memory size980.0 B
2023-12-12T15:41:11.181267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.7980769
Min length2

Characters and Unicode

Total characters499
Distinct characters179
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)95.2%

Sample

1st row합계(중복제외)
2nd row강릉교동
3rd row강릉역앞
4th row강문해변
5th row경포호
ValueCountFrequency (%)
합계(중복제외 5
 
4.8%
대두라도 1
 
1.0%
강릉역앞 1
 
1.0%
웅천못공원 1
 
1.0%
여수엑스포 1
 
1.0%
여서동사거리 1
 
1.0%
손죽도 1
 
1.0%
소호항 1
 
1.0%
백야도 1
 
1.0%
돌산우두리 1
 
1.0%
Other values (90) 90
86.5%
2023-12-12T15:41:11.656632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 19
 
3.8%
동 12
 
2.4%
대 11
 
2.2%
원 10
 
2.0%
포 10
 
2.0%
리 9
 
1.8%
산 9
 
1.8%
역 8
 
1.6%
수 8
 
1.6%
을 8
 
1.6%
Other values (169) 395
79.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 479
96.0%
Other Punctuation 10
 
2.0%
Close Punctuation 5
 
1.0%
Open Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 19
 
4.0%
동 12
 
2.5%
대 11
 
2.3%
원 10
 
2.1%
포 10
 
2.1%
리 9
 
1.9%
산 9
 
1.9%
역 8
 
1.7%
수 8
 
1.7%
을 8
 
1.7%
Other values (165) 375
78.3%
Other Punctuation
ValueCountFrequency (%)
, 5
50.0%
. 5
50.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 479
96.0%
Common 20
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 19
 
4.0%
동 12
 
2.5%
대 11
 
2.3%
원 10
 
2.1%
포 10
 
2.1%
리 9
 
1.9%
산 9
 
1.9%
역 8
 
1.7%
수 8
 
1.7%
을 8
 
1.7%
Other values (165) 375
78.3%
Common
ValueCountFrequency (%)
, 5
25.0%
. 5
25.0%
) 5
25.0%
( 5
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 479
96.0%
ASCII 20
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 19
 
4.0%
동 12
 
2.5%
대 11
 
2.3%
원 10
 
2.1%
포 10
 
2.1%
리 9
 
1.9%
산 9
 
1.9%
역 8
 
1.7%
수 8
 
1.7%
을 8
 
1.7%
Other values (165) 375
78.3%
ASCII
ValueCountFrequency (%)
, 5
25.0%
. 5
25.0%
) 5
25.0%
( 5
25.0%

싱글
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct90
Distinct (%)86.5%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean41.019231
Minimum14.3
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T15:41:11.853274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.3
5-th percentile20.855
Q132.975
median41.1
Q349.075
95-th percentile61.67
Maximum75
Range60.7
Interquartile range (IQR)16.1

Descriptive statistics

Standard deviation12.204059
Coefficient of variation (CV)0.29752041
Kurtosis-0.056955582
Mean41.019231
Median Absolute Deviation (MAD)8.15
Skewness0.12543478
Sum4266
Variance148.93904
MonotonicityNot monotonic
2023-12-12T15:41:12.064584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.6 3
 
2.8%
42.6 2
 
1.9%
33.3 2
 
1.9%
42.2 2
 
1.9%
50.1 2
 
1.9%
25.3 2
 
1.9%
39.4 2
 
1.9%
53.8 2
 
1.9%
45.8 2
 
1.9%
32.5 2
 
1.9%
Other values (80) 83
78.3%
ValueCountFrequency (%)
14.3 1
0.9%
16.0 1
0.9%
16.7 1
0.9%
17.5 1
0.9%
18.2 1
0.9%
20.6 1
0.9%
22.3 1
0.9%
23.5 1
0.9%
23.6 1
0.9%
24.2 1
0.9%
ValueCountFrequency (%)
75.0 1
0.9%
67.3 1
0.9%
65.7 1
0.9%
64.3 1
0.9%
63.9 1
0.9%
61.7 1
0.9%
61.5 1
0.9%
61.3 1
0.9%
58.1 1
0.9%
56.5 1
0.9%

신혼
Real number (ℝ)

MISSING  ZEROS 

Distinct40
Distinct (%)38.5%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean3.7980769
Minimum0
Maximum8.1
Zeros8
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T15:41:12.248858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.2
median4
Q34.7
95-th percentile5.4
Maximum8.1
Range8.1
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4949092
Coefficient of variation (CV)0.39359635
Kurtosis1.6156105
Mean3.7980769
Median Absolute Deviation (MAD)0.8
Skewness-0.88956298
Sum395
Variance2.2347535
MonotonicityNot monotonic
2023-12-12T15:41:12.475798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 8
 
7.5%
3.9 7
 
6.6%
4.0 6
 
5.7%
3.2 5
 
4.7%
5.1 5
 
4.7%
4.2 5
 
4.7%
4.7 4
 
3.8%
4.9 4
 
3.8%
4.4 4
 
3.8%
3.8 4
 
3.8%
Other values (30) 52
49.1%
ValueCountFrequency (%)
0.0 8
7.5%
1.5 1
 
0.9%
1.9 1
 
0.9%
2.1 1
 
0.9%
2.2 1
 
0.9%
2.3 1
 
0.9%
2.4 2
 
1.9%
2.6 1
 
0.9%
2.7 2
 
1.9%
2.8 1
 
0.9%
ValueCountFrequency (%)
8.1 1
 
0.9%
6.7 1
 
0.9%
6.2 1
 
0.9%
5.8 1
 
0.9%
5.7 1
 
0.9%
5.4 3
2.8%
5.3 1
 
0.9%
5.2 2
 
1.9%
5.1 5
4.7%
5.0 2
 
1.9%

영유아,어린이자녀가족
Real number (ℝ)

MISSING  ZEROS 

Distinct67
Distinct (%)64.4%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean10.207692
Minimum0
Maximum24.9
Zeros4
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T15:41:12.679882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.745
Q18.175
median9.55
Q311.6
95-th percentile18.27
Maximum24.9
Range24.9
Interquartile range (IQR)3.425

Descriptive statistics

Standard deviation4.2946047
Coefficient of variation (CV)0.42072239
Kurtosis1.8408161
Mean10.207692
Median Absolute Deviation (MAD)1.8
Skewness0.65924961
Sum1061.6
Variance18.44363
MonotonicityNot monotonic
2023-12-12T15:41:12.859636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.7 4
 
3.8%
0.0 4
 
3.8%
8.2 4
 
3.8%
9.6 3
 
2.8%
9.0 3
 
2.8%
10.6 3
 
2.8%
9.3 3
 
2.8%
6.5 3
 
2.8%
8.3 3
 
2.8%
9.2 3
 
2.8%
Other values (57) 71
67.0%
ValueCountFrequency (%)
0.0 4
3.8%
3.8 1
 
0.9%
5.7 1
 
0.9%
6.0 1
 
0.9%
6.1 1
 
0.9%
6.3 1
 
0.9%
6.4 2
1.9%
6.5 3
2.8%
6.7 2
1.9%
6.8 1
 
0.9%
ValueCountFrequency (%)
24.9 1
0.9%
21.9 1
0.9%
21.1 1
0.9%
20.1 1
0.9%
18.3 2
1.9%
18.1 1
0.9%
17.8 1
0.9%
17.1 1
0.9%
17.0 1
0.9%
16.3 1
0.9%

청소년자녀가족
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)76.0%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean25.131731
Minimum0
Maximum57.1
Zeros1
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T15:41:13.026517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.815
Q122.625
median25.15
Q328.5
95-th percentile33.8
Maximum57.1
Range57.1
Interquartile range (IQR)5.875

Descriptive statistics

Standard deviation6.7893873
Coefficient of variation (CV)0.270152
Kurtosis5.5062098
Mean25.131731
Median Absolute Deviation (MAD)3.3
Skewness0.3931236
Sum2613.7
Variance46.095779
MonotonicityNot monotonic
2023-12-12T15:41:13.192605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.6 3
 
2.8%
28.5 3
 
2.8%
23.3 3
 
2.8%
20.2 3
 
2.8%
26.4 2
 
1.9%
24.2 2
 
1.9%
28.9 2
 
1.9%
23.8 2
 
1.9%
33.8 2
 
1.9%
27.9 2
 
1.9%
Other values (69) 80
75.5%
ValueCountFrequency (%)
0.0 1
0.9%
9.1 1
0.9%
10.0 1
0.9%
13.3 1
0.9%
14.4 1
0.9%
15.8 1
0.9%
15.9 1
0.9%
16.4 1
0.9%
17.3 1
0.9%
17.4 2
1.9%
ValueCountFrequency (%)
57.1 1
0.9%
40.0 1
0.9%
37.5 1
0.9%
36.4 1
0.9%
34.9 1
0.9%
33.8 2
1.9%
32.7 1
0.9%
32.4 1
0.9%
32.3 1
0.9%
32.1 1
0.9%

성인자녀가족
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct77
Distinct (%)74.0%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean12.7
Minimum0
Maximum28.6
Zeros2
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T15:41:13.370510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.7
Q19.75
median12.05
Q315.425
95-th percentile21.885
Maximum28.6
Range28.6
Interquartile range (IQR)5.675

Descriptive statistics

Standard deviation5.0288681
Coefficient of variation (CV)0.39597387
Kurtosis0.92468074
Mean12.7
Median Absolute Deviation (MAD)2.7
Skewness0.51969308
Sum1320.8
Variance25.289515
MonotonicityNot monotonic
2023-12-12T15:41:13.568718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.2 4
 
3.8%
17.0 3
 
2.8%
6.9 3
 
2.8%
12.9 3
 
2.8%
8.3 3
 
2.8%
11.3 2
 
1.9%
10.6 2
 
1.9%
13.6 2
 
1.9%
11.2 2
 
1.9%
14.1 2
 
1.9%
Other values (67) 78
73.6%
ValueCountFrequency (%)
0.0 2
1.9%
3.6 1
 
0.9%
5.0 1
 
0.9%
6.3 1
 
0.9%
6.7 2
1.9%
6.8 1
 
0.9%
6.9 3
2.8%
7.0 1
 
0.9%
7.4 1
 
0.9%
7.5 1
 
0.9%
ValueCountFrequency (%)
28.6 1
0.9%
25.4 1
0.9%
25.0 1
0.9%
23.1 1
0.9%
23.0 1
0.9%
21.9 1
0.9%
21.8 1
0.9%
21.1 1
0.9%
21.0 1
0.9%
20.1 1
0.9%

실버
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct65
Distinct (%)62.5%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean7.1384615
Minimum0
Maximum45.5
Zeros2
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T15:41:13.748586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.79
Q14.175
median5.7
Q38.125
95-th percentile19.57
Maximum45.5
Range45.5
Interquartile range (IQR)3.95

Descriptive statistics

Standard deviation6.1098569
Coefficient of variation (CV)0.85590667
Kurtosis15.750369
Mean7.1384615
Median Absolute Deviation (MAD)1.95
Skewness3.3528772
Sum742.4
Variance37.330351
MonotonicityNot monotonic
2023-12-12T15:41:13.930571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 5
 
4.7%
5.7 4
 
3.8%
5.8 4
 
3.8%
5.2 4
 
3.8%
4.9 3
 
2.8%
5.9 3
 
2.8%
5.1 3
 
2.8%
3.3 3
 
2.8%
4.2 3
 
2.8%
4.5 3
 
2.8%
Other values (55) 69
65.1%
ValueCountFrequency (%)
0.0 2
1.9%
1.0 1
 
0.9%
1.5 1
 
0.9%
1.7 2
1.9%
2.3 1
 
0.9%
2.5 1
 
0.9%
2.9 1
 
0.9%
3.0 1
 
0.9%
3.1 3
2.8%
3.2 1
 
0.9%
ValueCountFrequency (%)
45.5 1
0.9%
25.0 2
1.9%
24.0 1
0.9%
20.0 1
0.9%
19.6 1
0.9%
19.4 1
0.9%
15.9 1
0.9%
15.4 1
0.9%
11.5 1
0.9%
11.1 1
0.9%

Interactions

2023-12-12T15:41:09.252931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.183640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.788224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.432824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.970028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.576323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.339107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.262966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.889575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.523467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.056445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.686376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.454047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.373757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.048202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.622912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.157405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.798254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.560635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.464015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.144815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.706069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.253011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.922228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.649580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.572767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.233696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.798494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.358756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.034761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.743851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:06.688395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.331051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:07.894460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:08.464311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:09.150624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:41:14.049038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체명관광지명싱글신혼영유아,어린이자녀가족청소년자녀가족성인자녀가족실버
지자체명1.0000.0000.6060.3680.5710.1790.3460.250
관광지명0.0001.0000.9780.9970.8431.0000.9930.988
싱글0.6060.9781.0000.5390.5720.8100.8240.595
신혼0.3680.9970.5391.0000.5100.5290.6100.481
영유아,어린이자녀가족0.5710.8430.5720.5101.0000.4690.6240.256
청소년자녀가족0.1791.0000.8100.5290.4691.0000.7170.610
성인자녀가족0.3460.9930.8240.6100.6240.7171.0000.635
실버0.2500.9880.5950.4810.2560.6100.6351.000
2023-12-12T15:41:14.210090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
싱글신혼영유아,어린이자녀가족청소년자녀가족성인자녀가족실버지자체명
싱글1.0000.109-0.308-0.788-0.671-0.6280.286
신혼0.1091.0000.278-0.086-0.193-0.3170.154
영유아,어린이자녀가족-0.3080.2781.0000.180-0.003-0.1060.264
청소년자녀가족-0.788-0.0860.1801.0000.4000.4130.098
성인자녀가족-0.671-0.193-0.0030.4001.0000.4510.144
실버-0.628-0.317-0.1060.4130.4511.0000.159
지자체명0.2860.1540.2640.0980.1440.1591.000

Missing values

2023-12-12T15:41:09.875099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:41:10.015256image/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.
2023-12-12T15:41:10.165277image/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

지자체명관광지명싱글신혼영유아,어린이자녀가족청소년자녀가족성인자녀가족실버
0강릉시합계(중복제외)46.84.09.623.411.34.8
1강릉시강릉교동50.43.99.024.09.43.1
2강릉시강릉역앞47.73.88.922.412.15.1
3강릉시강문해변55.34.510.619.66.93.1
4강릉시경포호55.13.89.020.28.73.3
5강릉시도깨비촬영지48.23.99.322.77.78.2
6강릉시안목해변45.24.09.823.912.05.1
7강릉시역전상권50.84.69.321.110.14.2
8강릉시영진항38.63.911.324.914.07.3
9강릉시오죽헌49.01.58.424.710.36.1
지자체명관광지명싱글신혼영유아,어린이자녀가족청소년자녀가족성인자녀가족실버
96전주시아중호수42.62.110.619.117.08.5
97전주시완산공원40.43.710.924.314.85.8
98전주시웨딩거리56.55.26.518.710.22.9
99전주시자연생태관50.13.89.024.96.85.2
100전주시전북도청41.34.39.228.411.94.9
101전주시전주동물원26.93.524.926.415.43.0
102전주시전주터미널42.82.86.424.515.57.9
103전주시한옥마을47.64.611.424.68.43.4
104<NA><NA><NA><NA><NA><NA><NA><NA>
105<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

지자체명관광지명싱글신혼영유아,어린이자녀가족청소년자녀가족성인자녀가족실버# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>2