Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells54
Missing cells (%)29.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory76.7 B

Variable types

Text1
Numeric7

Dataset

Description경남 창원시의 주요관광지 23개소(3.15아트, 돝섬유원지, 로봇랜드 등)의 내국인과 외국인의 입장객 통계입니다.
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15101613

Alerts

합계 is highly overall correlated with 2023년 01월(내국인) and 4 other fieldsHigh correlation
2023년 01월(내국인) is highly overall correlated with 합계 and 3 other fieldsHigh correlation
2023년 01월(외국인) is highly overall correlated with 합계 and 4 other fieldsHigh correlation
2023년 02월(내국인) is highly overall correlated with 합계 and 4 other fieldsHigh correlation
2023년 02월(외국인) is highly overall correlated with 합계 and 3 other fieldsHigh correlation
2023년 03월(내국인) is highly overall correlated with 합계 and 5 other fieldsHigh correlation
2023년 03월(외국인) is highly overall correlated with 2023년 03월(내국인)High correlation
2023년 01월(내국인) has 3 (13.0%) missing valuesMissing
2023년 01월(외국인) has 16 (69.6%) missing valuesMissing
2023년 02월(내국인) has 3 (13.0%) missing valuesMissing
2023년 02월(외국인) has 16 (69.6%) missing valuesMissing
2023년 03월(내국인) has 1 (4.3%) missing valuesMissing
2023년 03월(외국인) has 15 (65.2%) missing valuesMissing
관광지 has unique valuesUnique
합계 has unique valuesUnique
합계 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:46:00.919213
Analysis finished2023-12-10 23:46:05.639419
Duration4.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관광지
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T08:46:05.780235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.6956522
Min length4

Characters and Unicode

Total characters154
Distinct characters94
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

Unique23 ?
Unique (%)100.0%

Sample

1st row로봇랜드
2nd row용지호수공원 무빙보트
3rd row창원짚트랙
4th row3.15아트
5th row도립미술관
ValueCountFrequency (%)
로봇랜드 1
 
3.8%
용지호수공원 1
 
3.8%
창원단감테마공원 1
 
3.8%
진해내수면환경생태공원 1
 
3.8%
주기철목사기념관 1
 
3.8%
저도비치로드 1
 
3.8%
봉암유원지 1
 
3.8%
군항문화탐방 1
 
3.8%
스카이워크 1
 
3.8%
콰이강의다리 1
 
3.8%
Other values (16) 16
61.5%
2023-12-11T08:46:06.098280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 13
 
8.4%
트 6
 
3.9%
창 6
 
3.9%
공 5
 
3.2%
관 4
 
2.6%
지 4
 
2.6%
해 4
 
2.6%
드 3
 
1.9%
수 3
 
1.9%
3
 
1.9%
Other values (84) 103
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147
95.5%
Space Separator 3
 
1.9%
Decimal Number 3
 
1.9%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 13
 
8.8%
트 6
 
4.1%
창 6
 
4.1%
공 5
 
3.4%
관 4
 
2.7%
지 4
 
2.7%
해 4
 
2.7%
드 3
 
2.0%
수 3
 
2.0%
경 2
 
1.4%
Other values (79) 97
66.0%
Decimal Number
ValueCountFrequency (%)
5 1
33.3%
1 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147
95.5%
Common 7
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 13
 
8.8%
트 6
 
4.1%
창 6
 
4.1%
공 5
 
3.4%
관 4
 
2.7%
지 4
 
2.7%
해 4
 
2.7%
드 3
 
2.0%
수 3
 
2.0%
경 2
 
1.4%
Other values (79) 97
66.0%
Common
ValueCountFrequency (%)
3
42.9%
5 1
 
14.3%
1 1
 
14.3%
. 1
 
14.3%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147
95.5%
ASCII 7
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 13
 
8.8%
트 6
 
4.1%
창 6
 
4.1%
공 5
 
3.4%
관 4
 
2.7%
지 4
 
2.7%
해 4
 
2.7%
드 3
 
2.0%
수 3
 
2.0%
경 2
 
1.4%
Other values (79) 97
66.0%
ASCII
ValueCountFrequency (%)
3
42.9%
5 1
 
14.3%
1 1
 
14.3%
. 1
 
14.3%
3 1
 
14.3%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40008.043
Minimum0
Maximum170465
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:06.232095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile324.3
Q119719
median26303
Q344950
95-th percentile116041.9
Maximum170465
Range170465
Interquartile range (IQR)25231

Descriptive statistics

Standard deviation41540.89
Coefficient of variation (CV)1.0383135
Kurtosis3.6375482
Mean40008.043
Median Absolute Deviation (MAD)11793
Skewness1.8635504
Sum920185
Variance1.7256456 × 109
MonotonicityNot monotonic
2023-12-11T08:46:06.367281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
61966 1
 
4.3%
579 1
 
4.3%
51804 1
 
4.3%
23353 1
 
4.3%
117454 1
 
4.3%
8362 1
 
4.3%
26303 1
 
4.3%
38096 1
 
4.3%
296 1
 
4.3%
103333 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
296 1
4.3%
579 1
4.3%
5233 1
4.3%
8362 1
4.3%
17618 1
4.3%
21820 1
4.3%
22245 1
4.3%
23353 1
4.3%
25753 1
4.3%
ValueCountFrequency (%)
170465 1
4.3%
117454 1
4.3%
103333 1
4.3%
72178 1
4.3%
61966 1
4.3%
51804 1
4.3%
38096 1
4.3%
37443 1
4.3%
32733 1
4.3%
28764 1
4.3%

2023년 01월(내국인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12367.2
Minimum15
Maximum49577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:06.502228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile946.95
Q13961.5
median7293.5
Q320924.75
95-th percentile34143.3
Maximum49577
Range49562
Interquartile range (IQR)16963.25

Descriptive statistics

Standard deviation12506.359
Coefficient of variation (CV)1.0112523
Kurtosis3.0118065
Mean12367.2
Median Absolute Deviation (MAD)5667
Skewness1.6523466
Sum247344
Variance1.5640901 × 108
MonotonicityNot monotonic
2023-12-11T08:46:06.598480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5673 1
 
4.3%
20891 1
 
4.3%
5822 1
 
4.3%
21192 1
 
4.3%
1790 1
 
4.3%
7255 1
 
4.3%
10118 1
 
4.3%
15 1
 
4.3%
33331 1
 
4.3%
11188 1
 
4.3%
Other values (10) 10
43.5%
(Missing) 3
 
13.0%
ValueCountFrequency (%)
15 1
4.3%
996 1
4.3%
1463 1
4.3%
1790 1
4.3%
3009 1
4.3%
4279 1
4.3%
5673 1
4.3%
5822 1
4.3%
7227 1
4.3%
7255 1
4.3%
ValueCountFrequency (%)
49577 1
4.3%
33331 1
4.3%
21466 1
4.3%
21192 1
4.3%
21026 1
4.3%
20891 1
4.3%
13694 1
4.3%
11188 1
4.3%
10118 1
4.3%
7332 1
4.3%

2023년 01월(외국인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing16
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean38.857143
Minimum3
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:06.684270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.6
Q118
median39
Q357
95-th percentile77.6
Maximum80
Range77
Interquartile range (IQR)39

Descriptive statistics

Standard deviation28.794841
Coefficient of variation (CV)0.7410437
Kurtosis-1.2247895
Mean38.857143
Median Absolute Deviation (MAD)24
Skewness0.3978154
Sum272
Variance829.14286
MonotonicityNot monotonic
2023-12-11T08:46:06.782193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15 1
 
4.3%
3 1
 
4.3%
39 1
 
4.3%
21 1
 
4.3%
80 1
 
4.3%
72 1
 
4.3%
42 1
 
4.3%
(Missing) 16
69.6%
ValueCountFrequency (%)
3 1
4.3%
15 1
4.3%
21 1
4.3%
39 1
4.3%
42 1
4.3%
72 1
4.3%
80 1
4.3%
ValueCountFrequency (%)
80 1
4.3%
72 1
4.3%
42 1
4.3%
39 1
4.3%
21 1
4.3%
15 1
4.3%
3 1
4.3%

2023년 02월(내국인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean13055.8
Minimum110
Maximum50304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:06.871431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile1443.8
Q14256.75
median8760
Q317706
95-th percentile34411.45
Maximum50304
Range50194
Interquartile range (IQR)13449.25

Descriptive statistics

Standard deviation12582.846
Coefficient of variation (CV)0.96377445
Kurtosis2.8950172
Mean13055.8
Median Absolute Deviation (MAD)6263.5
Skewness1.6127785
Sum261116
Variance1.5832803 × 108
MonotonicityNot monotonic
2023-12-11T08:46:06.975784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6296 1
 
4.3%
15210 1
 
4.3%
7246 1
 
4.3%
22473 1
 
4.3%
2882 1
 
4.3%
9152 1
 
4.3%
11752 1
 
4.3%
110 1
 
4.3%
33575 1
 
4.3%
23143 1
 
4.3%
Other values (10) 10
43.5%
(Missing) 3
 
13.0%
ValueCountFrequency (%)
110 1
4.3%
1514 1
4.3%
1674 1
4.3%
2683 1
4.3%
2882 1
4.3%
4715 1
4.3%
6296 1
4.3%
7246 1
4.3%
7310 1
4.3%
8368 1
4.3%
ValueCountFrequency (%)
50304 1
4.3%
33575 1
4.3%
24541 1
4.3%
23143 1
4.3%
22473 1
4.3%
16117 1
4.3%
15210 1
4.3%
12051 1
4.3%
11752 1
4.3%
9152 1
4.3%

2023년 02월(외국인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing16
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean34.428571
Minimum6
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:07.062108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.8
Q122
median40
Q347
95-th percentile54.6
Maximum57
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.068798
Coefficient of variation (CV)0.55386549
Kurtosis-1.0786884
Mean34.428571
Median Absolute Deviation (MAD)9
Skewness-0.61665413
Sum241
Variance363.61905
MonotonicityNot monotonic
2023-12-11T08:46:07.157493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1
 
4.3%
57 1
 
4.3%
32 1
 
4.3%
49 1
 
4.3%
12 1
 
4.3%
45 1
 
4.3%
40 1
 
4.3%
(Missing) 16
69.6%
ValueCountFrequency (%)
6 1
4.3%
12 1
4.3%
32 1
4.3%
40 1
4.3%
45 1
4.3%
49 1
4.3%
57 1
4.3%
ValueCountFrequency (%)
57 1
4.3%
49 1
4.3%
45 1
4.3%
40 1
4.3%
32 1
4.3%
12 1
4.3%
6 1
4.3%

2023년 03월(내국인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean18521.5
Minimum171
Maximum73789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:07.468461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile651.5
Q17656.25
median12104.5
Q323922
95-th percentile68608.45
Maximum73789
Range73618
Interquartile range (IQR)16265.75

Descriptive statistics

Standard deviation19825.298
Coefficient of variation (CV)1.0703937
Kurtosis3.5377981
Mean18521.5
Median Absolute Deviation (MAD)6765.5
Skewness1.9260599
Sum407473
Variance3.9304243 × 108
MonotonicityNot monotonic
2023-12-11T08:46:07.564735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
27635 1
 
4.3%
28527 1
 
4.3%
15703 1
 
4.3%
10285 1
 
4.3%
73789 1
 
4.3%
3690 1
 
4.3%
9896 1
 
4.3%
16226 1
 
4.3%
171 1
 
4.3%
36241 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
171 1
4.3%
579 1
4.3%
2029 1
4.3%
2063 1
4.3%
3690 1
4.3%
6988 1
4.3%
9661 1
4.3%
9894 1
4.3%
9896 1
4.3%
10083 1
4.3%
ValueCountFrequency (%)
73789 1
4.3%
70312 1
4.3%
36241 1
4.3%
28527 1
4.3%
27635 1
4.3%
26171 1
4.3%
17175 1
4.3%
16431 1
4.3%
16226 1
4.3%
15703 1
4.3%

2023년 03월(외국인)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing15
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean467.375
Minimum10
Maximum3251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:46:07.650398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.9
Q147.25
median76
Q3113.75
95-th percentile2163.2
Maximum3251
Range3241
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation1125.5489
Coefficient of variation (CV)2.4082351
Kurtosis7.9661192
Mean467.375
Median Absolute Deviation (MAD)40
Skewness2.8204636
Sum3739
Variance1266860.3
MonotonicityNot monotonic
2023-12-11T08:46:07.730676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
24 1
 
4.3%
79 1
 
4.3%
3251 1
 
4.3%
143 1
 
4.3%
10 1
 
4.3%
55 1
 
4.3%
73 1
 
4.3%
104 1
 
4.3%
(Missing) 15
65.2%
ValueCountFrequency (%)
10 1
4.3%
24 1
4.3%
55 1
4.3%
73 1
4.3%
79 1
4.3%
104 1
4.3%
143 1
4.3%
3251 1
4.3%
ValueCountFrequency (%)
3251 1
4.3%
143 1
4.3%
104 1
4.3%
79 1
4.3%
73 1
4.3%
55 1
4.3%
24 1
4.3%
10 1
4.3%

Interactions

2023-12-11T08:46:04.732529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.190276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.823282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.631466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.123706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.634708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.148554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.810642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.274067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.903537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.708959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.190632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.710329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.243356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.883534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.352857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.983046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.780416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.256690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.779928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.325829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.973428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.450923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.079244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.853182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.328831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.850075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.426852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:05.053117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.538226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.159921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.917761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.391783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.917586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.499979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:05.132645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.612915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.228255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.983990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.454589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.984863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.571775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:05.213002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:01.713829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:02.555737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.054806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:03.525786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.077800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:04.656884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:46:07.800107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관광지합계2023년 01월(내국인)2023년 01월(외국인)2023년 02월(내국인)2023년 02월(외국인)2023년 03월(내국인)2023년 03월(외국인)
관광지1.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.7251.0000.9181.0000.6870.000
2023년 01월(내국인)1.0000.7251.0001.0000.9021.0000.9040.000
2023년 01월(외국인)1.0001.0001.0001.0001.0001.0001.0001.000
2023년 02월(내국인)1.0000.9180.9021.0001.0001.0000.7910.000
2023년 02월(외국인)1.0001.0001.0001.0001.0001.0001.0001.000
2023년 03월(내국인)1.0000.6870.9041.0000.7911.0001.0001.000
2023년 03월(외국인)1.0000.0000.0001.0000.0001.0001.0001.000
2023-12-11T08:46:07.900162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계2023년 01월(내국인)2023년 01월(외국인)2023년 02월(내국인)2023년 02월(외국인)2023년 03월(내국인)2023년 03월(외국인)
합계1.0000.8830.6070.9440.6070.8410.452
2023년 01월(내국인)0.8831.0000.5710.8410.4290.5760.036
2023년 01월(외국인)0.6070.5711.0000.7860.6570.7140.257
2023년 02월(내국인)0.9440.8410.7861.0000.6070.7680.143
2023년 02월(외국인)0.6070.4290.6570.6071.0000.5000.429
2023년 03월(내국인)0.8410.5760.7140.7680.5001.0000.643
2023년 03월(외국인)0.4520.0360.2570.1430.4290.6431.000

Missing values

2023-12-11T08:46:05.320881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:46:05.428212image/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.
2023-12-11T08:46:05.551094image/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

관광지합계2023년 01월(내국인)2023년 01월(외국인)2023년 02월(내국인)2023년 02월(외국인)2023년 03월(내국인)2023년 03월(외국인)
0로봇랜드6196611188<NA>23143<NA>27635<NA>
1용지호수공원 무빙보트579<NA><NA><NA><NA>579<NA>
2창원짚트랙0<NA><NA><NA><NA><NA><NA>
33.15아트287644279<NA>7310<NA>17175<NA>
4도립미술관2575321026152683<NA>2029<NA>
5돝섬유원지176183009<NA>4715<NA>9894<NA>
6문신미술관52331463316746206324
7성산아트홀3744372273916117571392479
8제황산공원 모노레일2224599621151432164313251
9진해해양공원1704654957780503044970312143
관광지합계2023년 01월(내국인)2023년 01월(외국인)2023년 02월(내국인)2023년 02월(외국인)2023년 03월(내국인)2023년 03월(외국인)
13주남저수지28582<NA><NA><NA><NA>2852755
14창원의집21820567372629645966173
15콰이강의다리 스카이워크1033333333142335754036241104
16군항문화탐방29615<NA>110<NA>171<NA>
17봉암유원지3809610118<NA>11752<NA>16226<NA>
18저도비치로드263037255<NA>9152<NA>9896<NA>
19주기철목사기념관83621790<NA>2882<NA>3690<NA>
20진해내수면환경생태공원11745421192<NA>22473<NA>73789<NA>
21창원단감테마공원233535822<NA>7246<NA>10285<NA>
22해양드라마세트장5180420891<NA>15210<NA>15703<NA>