Overview

Dataset statistics

Number of variables4
Number of observations43
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
Categorical1

Dataset

Description인천광역시 미추홀구 지방물가동향에 따른 데이터로 품목명, 조사규격, 평균금액, 기준 등의 항목을 제공하고 있습니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/3044096/fileData.do

Alerts

기준일 has constant value ""Constant
품목명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:04:30.810923
Analysis finished2023-12-12 13:04:31.214003
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T22:04:31.375623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.4651163
Min length2

Characters and Unicode

Total characters192
Distinct characters101
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row쓰레기봉투료
2nd row정화조청소료
3rd row설렁탕
4th row냉면
5th row비빔밥
ValueCountFrequency (%)
쓰레기봉투료 1
 
2.3%
라면_외식 1
 
2.3%
커피_외식 1
 
2.3%
국산차_외식 1
 
2.3%
세탁료 1
 
2.3%
의복수선료 1
 
2.3%
공동주택관리비 1
 
2.3%
택배이용료 1
 
2.3%
수영장이용료 1
 
2.3%
볼링장이용료 1
 
2.3%
Other values (33) 33
76.7%
2023-12-12T22:04:31.720421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 18
 
9.4%
용 10
 
5.2%
이 9
 
4.7%
_ 7
 
3.6%
장 6
 
3.1%
식 6
 
3.1%
외 6
 
3.1%
탕 4
 
2.1%
수 4
 
2.1%
비 4
 
2.1%
Other values (91) 118
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
95.3%
Connector Punctuation 7
 
3.6%
Uppercase Letter 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 18
 
9.8%
용 10
 
5.5%
이 9
 
4.9%
장 6
 
3.3%
식 6
 
3.3%
외 6
 
3.3%
탕 4
 
2.2%
수 4
 
2.2%
비 4
 
2.2%
밥 3
 
1.6%
Other values (88) 113
61.7%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
95.3%
Common 7
 
3.6%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 18
 
9.8%
용 10
 
5.5%
이 9
 
4.9%
장 6
 
3.3%
식 6
 
3.3%
외 6
 
3.3%
탕 4
 
2.2%
수 4
 
2.2%
비 4
 
2.2%
밥 3
 
1.6%
Other values (88) 113
61.7%
Latin
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%
Common
ValueCountFrequency (%)
_ 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
95.3%
ASCII 9
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 18
 
9.8%
용 10
 
5.5%
이 9
 
4.9%
장 6
 
3.3%
식 6
 
3.3%
외 6
 
3.3%
탕 4
 
2.2%
수 4
 
2.2%
비 4
 
2.2%
밥 3
 
1.6%
Other values (88) 113
61.7%
ASCII
ValueCountFrequency (%)
_ 7
77.8%
P 1
 
11.1%
C 1
 
11.1%

조사규격(통계청 기준)
Text

Distinct35
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T22:04:31.930216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length9.6046512
Min length2

Characters and Unicode

Total characters413
Distinct characters142
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

Unique34 ?
Unique (%)79.1%

Sample

1st row20리터
2nd row기본(750리터)
3rd row1인분_보통
4th row물냉면 1인분_보통
5th row1인분_보통
ValueCountFrequency (%)
1인분_보통 11
 
13.8%
성인 3
 
3.8%
200그램정도 2
 
2.5%
1인분 2
 
2.5%
신사복 2
 
2.5%
고기 2
 
2.5%
일반인 2
 
2.5%
일반 2
 
2.5%
무게_20키로그램이하 1
 
1.2%
성인일반 1
 
1.2%
Other values (52) 52
65.0%
2023-12-12T22:04:32.237605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
11.4%
인 23
 
5.6%
_ 23
 
5.6%
1 18
 
4.4%
분 14
 
3.4%
통 14
 
3.4%
보 13
 
3.1%
기 12
 
2.9%
반 11
 
2.7%
0 11
 
2.7%
Other values (132) 227
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
72.9%
Space Separator 47
 
11.4%
Decimal Number 39
 
9.4%
Connector Punctuation 23
 
5.6%
Uppercase Letter 1
 
0.2%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 23
 
7.6%
분 14
 
4.7%
통 14
 
4.7%
보 13
 
4.3%
기 12
 
4.0%
반 11
 
3.7%
일 10
 
3.3%
성 6
 
2.0%
그 5
 
1.7%
램 5
 
1.7%
Other values (120) 188
62.5%
Decimal Number
ValueCountFrequency (%)
1 18
46.2%
0 11
28.2%
2 6
 
15.4%
4 1
 
2.6%
3 1
 
2.6%
7 1
 
2.6%
5 1
 
2.6%
Space Separator
ValueCountFrequency (%)
47
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 23
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
72.9%
Common 111
 
26.9%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 23
 
7.6%
분 14
 
4.7%
통 14
 
4.7%
보 13
 
4.3%
기 12
 
4.0%
반 11
 
3.7%
일 10
 
3.3%
성 6
 
2.0%
그 5
 
1.7%
램 5
 
1.7%
Other values (120) 188
62.5%
Common
ValueCountFrequency (%)
47
42.3%
_ 23
20.7%
1 18
 
16.2%
0 11
 
9.9%
2 6
 
5.4%
4 1
 
0.9%
3 1
 
0.9%
7 1
 
0.9%
( 1
 
0.9%
5 1
 
0.9%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
72.9%
ASCII 112
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
42.0%
_ 23
20.5%
1 18
 
16.1%
0 11
 
9.8%
2 6
 
5.4%
4 1
 
0.9%
X 1
 
0.9%
3 1
 
0.9%
7 1
 
0.9%
( 1
 
0.9%
Other values (2) 2
 
1.8%
Hangul
ValueCountFrequency (%)
인 23
 
7.6%
분 14
 
4.7%
통 14
 
4.7%
보 13
 
4.3%
기 12
 
4.0%
반 11
 
3.7%
일 10
 
3.3%
성 6
 
2.0%
그 5
 
1.7%
램 5
 
1.7%
Other values (120) 188
62.5%

평균금액
Real number (ℝ)

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14593.023
Minimum460
Maximum115880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T22:04:32.382927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile1017
Q15350
median7430
Q315020
95-th percentile34110
Maximum115880
Range115420
Interquartile range (IQR)9670

Descriptive statistics

Standard deviation22034.961
Coefficient of variation (CV)1.5099654
Kurtosis14.387607
Mean14593.023
Median Absolute Deviation (MAD)3480
Skewness3.6919045
Sum627500
Variance4.8553949 × 108
MonotonicityNot monotonic
2023-12-12T22:04:32.502923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5350 2
 
4.7%
620 1
 
2.3%
9230 1
 
2.3%
2590 1
 
2.3%
7350 1
 
2.3%
4100 1
 
2.3%
115880 1
 
2.3%
7800 1
 
2.3%
10000 1
 
2.3%
4000 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
460 1
2.3%
620 1
2.3%
890 1
2.3%
2160 1
2.3%
2590 1
2.3%
2700 1
2.3%
4000 1
2.3%
4100 1
2.3%
4380 1
2.3%
5340 1
2.3%
ValueCountFrequency (%)
115880 1
2.3%
96670 1
2.3%
34380 1
2.3%
31680 1
2.3%
26000 1
2.3%
23500 1
2.3%
21050 1
2.3%
18890 1
2.3%
16400 1
2.3%
15440 1
2.3%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-09-12
43 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-12
2nd row2023-09-12
3rd row2023-09-12
4th row2023-09-12
5th row2023-09-12

Common Values

ValueCountFrequency (%)
2023-09-12 43
100.0%

Length

2023-12-12T22:04:32.870519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:04:32.956004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-12 43
100.0%

Interactions

2023-12-12T22:04:30.987014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:04:33.016289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목명조사규격(통계청 기준)평균금액
품목명1.0001.0001.000
조사규격(통계청 기준)1.0001.0000.972
평균금액1.0000.9721.000

Missing values

2023-12-12T22:04:31.101666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:04:31.180929image/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

품목명조사규격(통계청 기준)평균금액기준일
0쓰레기봉투료20리터6202023-09-12
1정화조청소료기본(750리터)210502023-09-12
2설렁탕1인분_보통80602023-09-12
3냉면물냉면 1인분_보통53502023-09-12
4비빔밥1인분_보통54002023-09-12
5갈비탕1인분_보통135602023-09-12
6삼계탕1인분_보통151402023-09-12
7김치찌개백반1인분_보통68002023-09-12
8된장찌개백반1인분_보통66502023-09-12
9불고기쇠고기200그램정도121202023-09-12
품목명조사규격(통계청 기준)평균금액기준일
33당구장이용료일반인 저녁시간92302023-09-12
34노래방이용료성인 저녁시간대 일반실260002023-09-12
35PC방이용료기본 1시간8902023-09-12
36사진촬영료반명함판 칼라_3X4센티149002023-09-12
37사진인화료인화요금4602023-09-12
38숙박료_여관독방 1박 욕탕부설343802023-09-12
39이용료성인96002023-09-12
40미용료성인여자 파마235002023-09-12
41목욕료성인기준64402023-09-12
42찜질방이용료성인 찜질복대여료 포함74302023-09-12