Overview

Dataset statistics

Number of variables4
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory36.1 B

Variable types

Text2
Numeric1
DateTime1

Dataset

Description충청남도 서산시 2022년 10월 기준 개인서비스 42개 품목(외식, 레저/스포츠.숙박료,미용료 등) 요금에 대한 물가를 조사하여 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=444&beforeMenuCd=DOM_000000201001001000&publicdatapk=15043668

Alerts

데이터 기준일자 has constant value ""Constant

Reproduction

Analysis started2024-01-09 19:45:28.937560
Analysis finished2024-01-09 19:45:29.248232
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size468.0 B
2024-01-10T04:45:29.359520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.6666667
Min length2

Characters and Unicode

Total characters196
Distinct characters103
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

Unique39 ?
Unique (%)92.9%

Sample

1st row설렁탕
2nd row냉면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
미용료 3
 
6.7%
백반 2
 
4.4%
당구장이용료 1
 
2.2%
볼링장이용료 1
 
2.2%
골프연습장이용료 1
 
2.2%
커피(아메리카노 1
 
2.2%
국산차(녹차 1
 
2.2%
세탁료 1
 
2.2%
의복수선료 1
 
2.2%
택배이용료 1
 
2.2%
Other values (32) 32
71.1%
2024-01-10T04:45:29.632439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 18
 
9.2%
용 11
 
5.6%
이 10
 
5.1%
( 6
 
3.1%
장 6
 
3.1%
) 6
 
3.1%
4
 
2.0%
탕 4
 
2.0%
수 4
 
2.0%
방 3
 
1.5%
Other values (93) 124
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 178
90.8%
Open Punctuation 6
 
3.1%
Close Punctuation 6
 
3.1%
Space Separator 4
 
2.0%
Uppercase Letter 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 18
 
10.1%
용 11
 
6.2%
이 10
 
5.6%
장 6
 
3.4%
탕 4
 
2.2%
수 4
 
2.2%
방 3
 
1.7%
면 3
 
1.7%
영 3
 
1.7%
미 3
 
1.7%
Other values (88) 113
63.5%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 178
90.8%
Common 16
 
8.2%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 18
 
10.1%
용 11
 
6.2%
이 10
 
5.6%
장 6
 
3.4%
탕 4
 
2.2%
수 4
 
2.2%
방 3
 
1.7%
면 3
 
1.7%
영 3
 
1.7%
미 3
 
1.7%
Other values (88) 113
63.5%
Common
ValueCountFrequency (%)
( 6
37.5%
) 6
37.5%
4
25.0%
Latin
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 178
90.8%
ASCII 18
 
9.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 18
 
10.1%
용 11
 
6.2%
이 10
 
5.6%
장 6
 
3.4%
탕 4
 
2.2%
수 4
 
2.2%
방 3
 
1.7%
면 3
 
1.7%
영 3
 
1.7%
미 3
 
1.7%
Other values (88) 113
63.5%
ASCII
ValueCountFrequency (%)
( 6
33.3%
) 6
33.3%
4
22.2%
P 1
 
5.6%
C 1
 
5.6%

단위
Text

Distinct23
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
2024-01-10T04:45:29.796053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length14
Mean length5.7857143
Min length2

Characters and Unicode

Total characters243
Distinct characters81
Distinct categories7 ?
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 (%)42.9%

Sample

1st row1그릇
2nd row1그릇
3rd row1인분
4th row1그릇
5th row1그릇
ValueCountFrequency (%)
1그릇 9
 
12.7%
1인분 8
 
11.3%
성인 7
 
9.9%
1회 7
 
9.9%
1시간 3
 
4.2%
1잔 2
 
2.8%
1벌 2
 
2.8%
일반인 2
 
2.8%
오후 1
 
1.4%
기본 1
 
1.4%
Other values (29) 29
40.8%
2024-01-10T04:45:30.060801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 42
17.3%
30
 
12.3%
인 17
 
7.0%
회 9
 
3.7%
그 9
 
3.7%
릇 9
 
3.7%
분 9
 
3.7%
성 7
 
2.9%
일 7
 
2.9%
시 4
 
1.6%
Other values (71) 100
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 145
59.7%
Decimal Number 50
 
20.6%
Space Separator 30
 
12.3%
Other Punctuation 8
 
3.3%
Lowercase Letter 6
 
2.5%
Open Punctuation 2
 
0.8%
Close Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 17
 
11.7%
회 9
 
6.2%
그 9
 
6.2%
릇 9
 
6.2%
분 9
 
6.2%
성 7
 
4.8%
일 7
 
4.8%
시 4
 
2.8%
이 4
 
2.8%
간 3
 
2.1%
Other values (55) 67
46.2%
Decimal Number
ValueCountFrequency (%)
1 42
84.0%
2 3
 
6.0%
0 2
 
4.0%
4 1
 
2.0%
3 1
 
2.0%
5 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
c 2
33.3%
m 2
33.3%
g 1
16.7%
k 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 4
50.0%
* 2
25.0%
. 2
25.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 145
59.7%
Common 92
37.9%
Latin 6
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 17
 
11.7%
회 9
 
6.2%
그 9
 
6.2%
릇 9
 
6.2%
분 9
 
6.2%
성 7
 
4.8%
일 7
 
4.8%
시 4
 
2.8%
이 4
 
2.8%
간 3
 
2.1%
Other values (55) 67
46.2%
Common
ValueCountFrequency (%)
1 42
45.7%
30
32.6%
, 4
 
4.3%
2 3
 
3.3%
0 2
 
2.2%
* 2
 
2.2%
. 2
 
2.2%
( 2
 
2.2%
) 2
 
2.2%
4 1
 
1.1%
Other values (2) 2
 
2.2%
Latin
ValueCountFrequency (%)
c 2
33.3%
m 2
33.3%
g 1
16.7%
k 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 145
59.7%
ASCII 98
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 42
42.9%
30
30.6%
, 4
 
4.1%
2 3
 
3.1%
0 2
 
2.0%
* 2
 
2.0%
c 2
 
2.0%
. 2
 
2.0%
( 2
 
2.0%
) 2
 
2.0%
Other values (6) 7
 
7.1%
Hangul
ValueCountFrequency (%)
인 17
 
11.7%
회 9
 
6.2%
그 9
 
6.2%
릇 9
 
6.2%
분 9
 
6.2%
성 7
 
4.8%
일 7
 
4.8%
시 4
 
2.8%
이 4
 
2.8%
간 3
 
2.1%
Other values (55) 67
46.2%

가격
Real number (ℝ)

Distinct22
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12178.571
Minimum500
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-01-10T04:45:30.154335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1575
Q17000
median9500
Q315000
95-th percentile31745
Maximum50000
Range49500
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation10042.994
Coefficient of variation (CV)0.82464465
Kurtosis4.94356
Mean12178.571
Median Absolute Deviation (MAD)5450
Skewness2.0019045
Sum511500
Variance1.0086172 × 108
MonotonicityNot monotonic
2024-01-10T04:45:30.239099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
15000 5
11.9%
8000 5
11.9%
9000 4
 
9.5%
10000 3
 
7.1%
12000 3
 
7.1%
20000 3
 
7.1%
7000 2
 
4.8%
3500 2
 
4.8%
3000 2
 
4.8%
50000 1
 
2.4%
Other values (12) 12
28.6%
ValueCountFrequency (%)
500 1
 
2.4%
1000 1
 
2.4%
1500 1
 
2.4%
3000 2
 
4.8%
3500 2
 
4.8%
4000 1
 
2.4%
4100 1
 
2.4%
6000 1
 
2.4%
7000 2
 
4.8%
8000 5
11.9%
ValueCountFrequency (%)
50000 1
 
2.4%
40000 1
 
2.4%
32000 1
 
2.4%
26900 1
 
2.4%
20000 3
7.1%
18000 1
 
2.4%
15000 5
11.9%
13000 1
 
2.4%
12000 3
7.1%
10500 1
 
2.4%

데이터 기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
Minimum2022-10-28 00:00:00
Maximum2022-10-28 00:00:00
2024-01-10T04:45:30.311196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:30.381686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T04:45:29.067492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T04:45:30.434261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목단위가격
품목1.0000.0000.000
단위0.0001.0000.893
가격0.0000.8931.000

Missing values

2024-01-10T04:45:29.157156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:45:29.222261image/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

품목단위가격데이터 기준일자
0설렁탕1그릇100002022-10-28
1냉면1그릇80002022-10-28
2비빔밥1인분90002022-10-28
3갈비탕1그릇120002022-10-28
4삼계탕1그릇150002022-10-28
5김치찌개 백반1인분90002022-10-28
6된장찌개 백반1인분90002022-10-28
7불고기(한우)1인분120002022-10-28
8등심구이(한우)1인분320002022-10-28
9돼지갈비1인분150002022-10-28
품목단위가격데이터 기준일자
32PC방이용료1시간10002022-10-28
33영화관람료성인 1회130002022-10-28
34사진촬영료3*4cm 1조200002022-10-28
35사진인화료10.2*15.2cm 1장5002022-10-28
36숙박료(모텔)1일400002022-10-28
37미용료성인 커트 1회150002022-10-28
38미용료성인 1회(파마)500002022-10-28
39미용료성인 1회(드라이)120002022-10-28
40목욕료성인 1회70002022-10-28
41찜질방이용료성인 1회90002022-10-28