Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory58.0 B

Variable types

Categorical1
Text1
Numeric4

Dataset

Description천안상록리조트 이용정보 내용(골프장, 호텔, 유스호스텔, 공원, 아쿠아피아 등 성인기준 이용금액)에 대한 데이터로 공무원 및 일반인 이용금액으로 구분됩니다.
URLhttps://www.data.go.kr/data/15054113/fileData.do

Alerts

공무원(주중) is highly overall correlated with 공무원(주말) and 2 other fieldsHigh correlation
공무원(주말) is highly overall correlated with 공무원(주중) and 2 other fieldsHigh correlation
일반인(주중) is highly overall correlated with 공무원(주중) and 3 other fieldsHigh correlation
일반인(주말) is highly overall correlated with 공무원(주중) and 3 other fieldsHigh correlation
구분 is highly overall correlated with 일반인(주중) and 1 other fieldsHigh correlation
공무원(주중) has 1 (4.5%) zerosZeros
일반인(주중) has 1 (4.5%) zerosZeros

Reproduction

Analysis started2023-12-12 11:56:00.568685
Analysis finished2023-12-12 11:56:02.894067
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
아쿠아피아
7 
호텔
5 
유스호스텔
4 
공원입장료
3 
골프장
3 

Length

Max length5
Median length5
Mean length4.0454545
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아쿠아피아 7
31.8%
호텔 5
22.7%
유스호스텔 4
18.2%
공원입장료 3
13.6%
골프장 3
13.6%

Length

2023-12-12T20:56:02.986373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:56:03.214288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아쿠아피아 7
31.8%
호텔 5
22.7%
유스호스텔 4
18.2%
공원입장료 3
13.6%
골프장 3
13.6%

내용
Text

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T20:56:03.473112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length5.5
Min length3

Characters and Unicode

Total characters121
Distinct characters50
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)81.8%

Sample

1st rowONDOL
2nd rowTWIN
3rd rowDELUXETWIN
4th rowQUEENTWIN
5th rowSUITE
ValueCountFrequency (%)
a타입 2
 
9.1%
s타입 2
 
9.1%
ondol 1
 
4.5%
미들시즌(대인 1
 
4.5%
카트료 1
 
4.5%
그린피 1
 
4.5%
동절기시즌(소인 1
 
4.5%
동절기시즌(대인 1
 
4.5%
하이시즌(소인 1
 
4.5%
하이시즌(대인 1
 
4.5%
Other values (10) 10
45.5%
2023-12-12T20:56:03.885282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 7
 
5.8%
즌 7
 
5.8%
인 6
 
5.0%
( 6
 
5.0%
) 6
 
5.0%
N 5
 
4.1%
I 5
 
4.1%
E 5
 
4.1%
T 4
 
3.3%
입 4
 
3.3%
Other values (40) 66
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
56.2%
Uppercase Letter 40
33.1%
Open Punctuation 6
 
5.0%
Close Punctuation 6
 
5.0%
Decimal Number 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7
 
10.3%
즌 7
 
10.3%
인 6
 
8.8%
입 4
 
5.9%
타 4
 
5.9%
이 3
 
4.4%
소 3
 
4.4%
기 3
 
4.4%
대 3
 
4.4%
들 2
 
2.9%
Other values (22) 26
38.2%
Uppercase Letter
ValueCountFrequency (%)
N 5
12.5%
I 5
12.5%
E 5
12.5%
T 4
10.0%
U 3
7.5%
W 3
7.5%
S 3
7.5%
A 2
 
5.0%
O 2
 
5.0%
L 2
 
5.0%
Other values (5) 6
15.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
56.2%
Latin 40
33.1%
Common 13
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7
 
10.3%
즌 7
 
10.3%
인 6
 
8.8%
입 4
 
5.9%
타 4
 
5.9%
이 3
 
4.4%
소 3
 
4.4%
기 3
 
4.4%
대 3
 
4.4%
들 2
 
2.9%
Other values (22) 26
38.2%
Latin
ValueCountFrequency (%)
N 5
12.5%
I 5
12.5%
E 5
12.5%
T 4
10.0%
U 3
7.5%
W 3
7.5%
S 3
7.5%
A 2
 
5.0%
O 2
 
5.0%
L 2
 
5.0%
Other values (5) 6
15.0%
Common
ValueCountFrequency (%)
( 6
46.2%
) 6
46.2%
5 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
56.2%
ASCII 53
43.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 7
 
10.3%
즌 7
 
10.3%
인 6
 
8.8%
입 4
 
5.9%
타 4
 
5.9%
이 3
 
4.4%
소 3
 
4.4%
기 3
 
4.4%
대 3
 
4.4%
들 2
 
2.9%
Other values (22) 26
38.2%
ASCII
ValueCountFrequency (%)
( 6
11.3%
) 6
11.3%
N 5
9.4%
I 5
9.4%
E 5
9.4%
T 4
 
7.5%
U 3
 
5.7%
W 3
 
5.7%
S 3
 
5.7%
A 2
 
3.8%
Other values (8) 11
20.8%

공무원(주중)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59568.182
Minimum0
Maximum140000
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:56:04.050940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4150
Q113625
median76000
Q379000
95-th percentile140000
Maximum140000
Range140000
Interquartile range (IQR)65375

Descriptive statistics

Standard deviation49537.455
Coefficient of variation (CV)0.83160931
Kurtosis-1.1143571
Mean59568.182
Median Absolute Deviation (MAD)60000
Skewness0.4433466
Sum1310500
Variance2.4539594 × 109
MonotonicityNot monotonic
2023-12-12T20:56:04.199160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
76000 6
27.3%
140000 4
18.2%
12000 2
 
9.1%
4000 1
 
4.5%
7000 1
 
4.5%
13500 1
 
4.5%
0 1
 
4.5%
18000 1
 
4.5%
14000 1
 
4.5%
25000 1
 
4.5%
Other values (3) 3
13.6%
ValueCountFrequency (%)
0 1
 
4.5%
4000 1
 
4.5%
7000 1
 
4.5%
12000 2
 
9.1%
13500 1
 
4.5%
14000 1
 
4.5%
18000 1
 
4.5%
19000 1
 
4.5%
25000 1
 
4.5%
76000 6
27.3%
ValueCountFrequency (%)
140000 4
18.2%
90000 1
 
4.5%
80000 1
 
4.5%
76000 6
27.3%
25000 1
 
4.5%
19000 1
 
4.5%
18000 1
 
4.5%
14000 1
 
4.5%
13500 1
 
4.5%
12000 2
 
9.1%

공무원(주말)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62204.545
Minimum4000
Maximum140000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:56:04.368647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile7325
Q116250
median76000
Q379000
95-th percentile140000
Maximum140000
Range136000
Interquartile range (IQR)62750

Descriptive statistics

Standard deviation49472.614
Coefficient of variation (CV)0.79532152
Kurtosis-1.2447008
Mean62204.545
Median Absolute Deviation (MAD)58000
Skewness0.43835309
Sum1368500
Variance2.4475395 × 109
MonotonicityNot monotonic
2023-12-12T20:56:04.543310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
76000 6
27.3%
140000 4
18.2%
15000 2
 
9.1%
4000 1
 
4.5%
7000 1
 
4.5%
13500 1
 
4.5%
16000 1
 
4.5%
21000 1
 
4.5%
17000 1
 
4.5%
25000 1
 
4.5%
Other values (3) 3
13.6%
ValueCountFrequency (%)
4000 1
 
4.5%
7000 1
 
4.5%
13500 1
 
4.5%
15000 2
 
9.1%
16000 1
 
4.5%
17000 1
 
4.5%
19000 1
 
4.5%
21000 1
 
4.5%
25000 1
 
4.5%
76000 6
27.3%
ValueCountFrequency (%)
140000 4
18.2%
120000 1
 
4.5%
80000 1
 
4.5%
76000 6
27.3%
25000 1
 
4.5%
21000 1
 
4.5%
19000 1
 
4.5%
17000 1
 
4.5%
16000 1
 
4.5%
15000 2
 
9.1%

일반인(주중)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108181.82
Minimum0
Maximum280000
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:56:04.708478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4500
Q129000
median110000
Q3152000
95-th percentile280000
Maximum280000
Range280000
Interquartile range (IQR)123000

Descriptive statistics

Standard deviation91271.774
Coefficient of variation (CV)0.84368867
Kurtosis-0.55032889
Mean108181.82
Median Absolute Deviation (MAD)73000
Skewness0.66094403
Sum2380000
Variance8.3305368 × 109
MonotonicityNot monotonic
2023-12-12T20:56:04.829797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
152000 6
27.3%
280000 3
13.6%
4000 1
 
4.5%
14000 1
 
4.5%
27000 1
 
4.5%
0 1
 
4.5%
36000 1
 
4.5%
28000 1
 
4.5%
50000 1
 
4.5%
38000 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
0 1
4.5%
4000 1
4.5%
14000 1
4.5%
24000 1
4.5%
27000 1
4.5%
28000 1
4.5%
32000 1
4.5%
36000 1
4.5%
38000 1
4.5%
50000 1
4.5%
ValueCountFrequency (%)
280000 3
13.6%
155000 1
 
4.5%
152000 6
27.3%
140000 1
 
4.5%
80000 1
 
4.5%
50000 1
 
4.5%
38000 1
 
4.5%
36000 1
 
4.5%
32000 1
 
4.5%
28000 1
 
4.5%

일반인(주말)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112681.82
Minimum4000
Maximum280000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:56:04.961664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile14650
Q134500
median110000
Q3152000
95-th percentile280000
Maximum280000
Range276000
Interquartile range (IQR)117500

Descriptive statistics

Standard deviation90372.45
Coefficient of variation (CV)0.80201448
Kurtosis-0.67998173
Mean112681.82
Median Absolute Deviation (MAD)73000
Skewness0.64937896
Sum2479000
Variance8.1671797 × 109
MonotonicityNot monotonic
2023-12-12T20:56:05.418530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
152000 6
27.3%
280000 3
13.6%
4000 1
 
4.5%
14000 1
 
4.5%
27000 1
 
4.5%
32000 1
 
4.5%
42000 1
 
4.5%
34000 1
 
4.5%
50000 1
 
4.5%
38000 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
4000 1
4.5%
14000 1
4.5%
27000 1
4.5%
30000 1
4.5%
32000 1
4.5%
34000 1
4.5%
36000 1
4.5%
38000 1
4.5%
42000 1
4.5%
50000 1
4.5%
ValueCountFrequency (%)
280000 3
13.6%
200000 1
 
4.5%
152000 6
27.3%
140000 1
 
4.5%
80000 1
 
4.5%
50000 1
 
4.5%
42000 1
 
4.5%
38000 1
 
4.5%
36000 1
 
4.5%
34000 1
 
4.5%

Interactions

2023-12-12T20:56:02.240966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:00.850114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.345410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.755606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:02.382490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:00.964591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.453886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.873157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:02.501046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.089286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.560539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:02.001544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:02.589588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.234500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:01.658536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:56:02.135465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:56:05.539616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분내용공무원(주중)공무원(주말)일반인(주중)일반인(주말)
구분1.0001.0000.8600.8450.7260.833
내용1.0001.0000.8380.8030.8120.878
공무원(주중)0.8600.8381.0001.0000.8450.907
공무원(주말)0.8450.8031.0001.0000.8400.907
일반인(주중)0.7260.8120.8450.8401.0000.955
일반인(주말)0.8330.8780.9070.9070.9551.000
2023-12-12T20:56:05.680091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공무원(주중)공무원(주말)일반인(주중)일반인(주말)구분
공무원(주중)1.0000.9780.9180.9010.493
공무원(주말)0.9781.0000.8990.9180.493
일반인(주중)0.9180.8991.0000.9860.569
일반인(주말)0.9010.9180.9861.0000.703
구분0.4930.4930.5690.7031.000

Missing values

2023-12-12T20:56:02.727878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:56:02.842117image/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

구분내용공무원(주중)공무원(주말)일반인(주중)일반인(주말)
0호텔ONDOL7600076000152000152000
1호텔TWIN7600076000152000152000
2호텔DELUXETWIN7600076000152000152000
3호텔QUEENTWIN7600076000152000152000
4호텔SUITE140000140000280000280000
5유스호스텔A타입7600076000152000152000
6유스호스텔A타입140000140000280000280000
7유스호스텔S타입7600076000152000152000
8유스호스텔S타입140000140000280000280000
9공원입장료개별기종4000400040004000
구분내용공무원(주중)공무원(주말)일반인(주중)일반인(주말)
12아쿠아피아로우시즌016000032000
13아쿠아피아미들시즌(대인)18000210003600042000
14아쿠아피아미들시즌(소인)14000170002800034000
15아쿠아피아하이시즌(대인)25000250005000050000
16아쿠아피아하이시즌(소인)19000190003800038000
17아쿠아피아동절기시즌(대인)12000150003200036000
18아쿠아피아동절기시즌(소인)12000150002400030000
19골프장그린피90000120000155000200000
20골프장카트료80000800008000080000
21골프장캐디비140000140000140000140000