Overview

Dataset statistics

Number of variables5
Number of observations43
Missing cells18
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory46.1 B

Variable types

Text2
Numeric3

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

대산읍 is highly overall correlated with 석남동 and 1 other fieldsHigh correlation
석남동 is highly overall correlated with 대산읍 and 1 other fieldsHigh correlation
동문동 is highly overall correlated with 대산읍 and 1 other fieldsHigh correlation
대산읍 has 2 (4.7%) missing valuesMissing
석남동 has 13 (30.2%) missing valuesMissing
동문동 has 3 (7.0%) missing valuesMissing

Reproduction

Analysis started2024-01-09 19:45:25.740484
Analysis finished2024-01-09 19:45:26.837838
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-01-10T04:45:26.958638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.7906977
Min length2

Characters and Unicode

Total characters206
Distinct characters107
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)95.3%

Sample

1st row설렁탕
2nd row냉면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
미용료 2
 
4.3%
백반 2
 
4.3%
볼링장이용료 1
 
2.1%
커피(아메리카노 1
 
2.1%
목욕료 1
 
2.1%
당구장이용료 1
 
2.1%
국산차(녹차 1
 
2.1%
세탁료 1
 
2.1%
의복수선료 1
 
2.1%
공공주택관리비 1
 
2.1%
Other values (35) 35
74.5%
2024-01-10T04:45:27.236433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 18
 
8.7%
이 11
 
5.3%
용 11
 
5.3%
) 6
 
2.9%
장 6
 
2.9%
( 6
 
2.9%
5
 
2.4%
수 4
 
1.9%
탕 4
 
1.9%
비 4
 
1.9%
Other values (97) 131
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
90.3%
Close Punctuation 6
 
2.9%
Open Punctuation 6
 
2.9%
Space Separator 5
 
2.4%
Uppercase Letter 2
 
1.0%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 18
 
9.7%
이 11
 
5.9%
용 11
 
5.9%
장 6
 
3.2%
수 4
 
2.2%
탕 4
 
2.2%
비 4
 
2.2%
면 3
 
1.6%
방 3
 
1.6%
밥 3
 
1.6%
Other values (91) 119
64.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 186
90.3%
Common 18
 
8.7%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 18
 
9.7%
이 11
 
5.9%
용 11
 
5.9%
장 6
 
3.2%
수 4
 
2.2%
탕 4
 
2.2%
비 4
 
2.2%
면 3
 
1.6%
방 3
 
1.6%
밥 3
 
1.6%
Other values (91) 119
64.0%
Common
ValueCountFrequency (%)
) 6
33.3%
( 6
33.3%
5
27.8%
1 1
 
5.6%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 186
90.3%
ASCII 20
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 18
 
9.7%
이 11
 
5.9%
용 11
 
5.9%
장 6
 
3.2%
수 4
 
2.2%
탕 4
 
2.2%
비 4
 
2.2%
면 3
 
1.6%
방 3
 
1.6%
밥 3
 
1.6%
Other values (91) 119
64.0%
ASCII
ValueCountFrequency (%)
) 6
30.0%
( 6
30.0%
5
25.0%
C 1
 
5.0%
P 1
 
5.0%
1 1
 
5.0%

단위
Text

Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-01-10T04:45:27.578635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length5.7209302
Min length2

Characters and Unicode

Total characters246
Distinct characters80
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 (%)41.9%

Sample

1st row1그릇
2nd row1그릇
3rd row1인분
4th row1그릇
5th row1그릇
ValueCountFrequency (%)
1그릇 9
 
12.9%
1회 7
 
10.0%
성인 7
 
10.0%
200g 4
 
5.7%
1인분 4
 
5.7%
1시간 3
 
4.3%
1벌 2
 
2.9%
일반인 2
 
2.9%
1잔 2
 
2.9%
20kg 1
 
1.4%
Other values (29) 29
41.4%
2024-01-10T04:45:27.845711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 37
 
15.0%
27
 
11.0%
인 13
 
5.3%
0 10
 
4.1%
릇 9
 
3.7%
회 9
 
3.7%
그 9
 
3.7%
2 8
 
3.3%
일 8
 
3.3%
성 7
 
2.8%
Other values (70) 109
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137
55.7%
Decimal Number 59
24.0%
Space Separator 27
 
11.0%
Lowercase Letter 10
 
4.1%
Other Punctuation 9
 
3.7%
Open Punctuation 2
 
0.8%
Close Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 13
 
9.5%
릇 9
 
6.6%
회 9
 
6.6%
그 9
 
6.6%
일 8
 
5.8%
성 7
 
5.1%
분 5
 
3.6%
시 4
 
2.9%
평 3
 
2.2%
리 3
 
2.2%
Other values (54) 67
48.9%
Decimal Number
ValueCountFrequency (%)
1 37
62.7%
0 10
 
16.9%
2 8
 
13.6%
4 2
 
3.4%
3 1
 
1.7%
5 1
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
g 5
50.0%
m 2
 
20.0%
c 2
 
20.0%
k 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 5
55.6%
* 2
 
22.2%
. 2
 
22.2%
Space Separator
ValueCountFrequency (%)
27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 137
55.7%
Common 99
40.2%
Latin 10
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 13
 
9.5%
릇 9
 
6.6%
회 9
 
6.6%
그 9
 
6.6%
일 8
 
5.8%
성 7
 
5.1%
분 5
 
3.6%
시 4
 
2.9%
평 3
 
2.2%
리 3
 
2.2%
Other values (54) 67
48.9%
Common
ValueCountFrequency (%)
1 37
37.4%
27
27.3%
0 10
 
10.1%
2 8
 
8.1%
, 5
 
5.1%
( 2
 
2.0%
* 2
 
2.0%
. 2
 
2.0%
4 2
 
2.0%
) 2
 
2.0%
Other values (2) 2
 
2.0%
Latin
ValueCountFrequency (%)
g 5
50.0%
m 2
 
20.0%
c 2
 
20.0%
k 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 137
55.7%
ASCII 109
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37
33.9%
27
24.8%
0 10
 
9.2%
2 8
 
7.3%
, 5
 
4.6%
g 5
 
4.6%
m 2
 
1.8%
( 2
 
1.8%
* 2
 
1.8%
c 2
 
1.8%
Other values (6) 9
 
8.3%
Hangul
ValueCountFrequency (%)
인 13
 
9.5%
릇 9
 
6.6%
회 9
 
6.6%
그 9
 
6.6%
일 8
 
5.8%
성 7
 
5.1%
분 5
 
3.6%
시 4
 
2.9%
평 3
 
2.2%
리 3
 
2.2%
Other values (54) 67
48.9%

대산읍
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)58.5%
Missing2
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean11912.195
Minimum300
Maximum67200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-01-10T04:45:27.939412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1500
Q15000
median8000
Q313000
95-th percentile40000
Maximum67200
Range66900
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation13238.489
Coefficient of variation (CV)1.1113392
Kurtosis8.3088829
Mean11912.195
Median Absolute Deviation (MAD)4600
Skewness2.7066765
Sum488400
Variance1.752576 × 108
MonotonicityNot monotonic
2024-01-10T04:45:28.025550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8000 5
 
11.6%
5000 4
 
9.3%
12000 3
 
7.0%
6000 3
 
7.0%
13000 3
 
7.0%
20000 2
 
4.7%
1500 2
 
4.7%
17000 2
 
4.7%
4000 2
 
4.7%
5500 1
 
2.3%
Other values (14) 14
32.6%
(Missing) 2
 
4.7%
ValueCountFrequency (%)
300 1
 
2.3%
1500 2
4.7%
2000 1
 
2.3%
2700 1
 
2.3%
3000 1
 
2.3%
3200 1
 
2.3%
3400 1
 
2.3%
4000 2
4.7%
5000 4
9.3%
5500 1
 
2.3%
ValueCountFrequency (%)
67200 1
 
2.3%
50000 1
 
2.3%
40000 1
 
2.3%
25000 1
 
2.3%
20000 2
4.7%
17000 2
4.7%
16900 1
 
2.3%
14000 1
 
2.3%
13000 3
7.0%
12000 3
7.0%

석남동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)56.7%
Missing13
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean14883.333
Minimum2000
Maximum140000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-01-10T04:45:28.107554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2725
Q16000
median6500
Q313750
95-th percentile46500
Maximum140000
Range138000
Interquartile range (IQR)7750

Descriptive statistics

Standard deviation26094.22
Coefficient of variation (CV)1.7532511
Kurtosis19.484235
Mean14883.333
Median Absolute Deviation (MAD)3000
Skewness4.2336818
Sum446500
Variance6.8090833 × 108
MonotonicityNot monotonic
2024-01-10T04:45:28.192227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6000 8
18.6%
15000 3
 
7.0%
5000 2
 
4.7%
3500 2
 
4.7%
8000 2
 
4.7%
7000 2
 
4.7%
12000 1
 
2.3%
10000 1
 
2.3%
30000 1
 
2.3%
14000 1
 
2.3%
Other values (7) 7
16.3%
(Missing) 13
30.2%
ValueCountFrequency (%)
2000 1
 
2.3%
2500 1
 
2.3%
3000 1
 
2.3%
3500 2
 
4.7%
5000 2
 
4.7%
6000 8
18.6%
7000 2
 
4.7%
8000 2
 
4.7%
10000 1
 
2.3%
12000 1
 
2.3%
ValueCountFrequency (%)
140000 1
 
2.3%
60000 1
 
2.3%
30000 1
 
2.3%
20000 1
 
2.3%
15000 3
7.0%
14000 1
 
2.3%
13000 1
 
2.3%
12000 1
 
2.3%
10000 1
 
2.3%
8000 2
4.7%

동문동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)57.5%
Missing3
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean11484.5
Minimum300
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-01-10T04:45:28.281358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1950
Q15000
median8000
Q313000
95-th percentile32294
Maximum40000
Range39700
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation9729.093
Coefficient of variation (CV)0.8471499
Kurtosis1.9686711
Mean11484.5
Median Absolute Deviation (MAD)3950
Skewness1.5759873
Sum459380
Variance94655251
MonotonicityNot monotonic
2024-01-10T04:45:28.378234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5000 5
 
11.6%
8000 4
 
9.3%
13000 3
 
7.0%
6000 3
 
7.0%
7000 3
 
7.0%
12000 3
 
7.0%
10000 2
 
4.7%
20000 2
 
4.7%
37880 1
 
2.3%
40000 1
 
2.3%
Other values (13) 13
30.2%
(Missing) 3
 
7.0%
ValueCountFrequency (%)
300 1
 
2.3%
1000 1
 
2.3%
2000 1
 
2.3%
3000 1
 
2.3%
3900 1
 
2.3%
4100 1
 
2.3%
4300 1
 
2.3%
5000 5
11.6%
6000 3
7.0%
7000 3
7.0%
ValueCountFrequency (%)
40000 1
 
2.3%
37880 1
 
2.3%
32000 1
 
2.3%
30000 1
 
2.3%
25900 1
 
2.3%
20000 2
4.7%
19000 1
 
2.3%
16000 1
 
2.3%
13000 3
7.0%
12000 3
7.0%

Interactions

2024-01-10T04:45:26.397841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:25.961269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.177189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.489840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.038498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.252627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.563093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.105525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:45:26.321937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T04:45:28.443817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목단위대산읍석남동동문동
품목1.0000.7460.0000.0000.583
단위0.7461.0000.9130.9280.910
대산읍0.0000.9131.0000.9410.852
석남동0.0000.9280.9411.0000.890
동문동0.5830.9100.8520.8901.000
2024-01-10T04:45:28.515408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대산읍석남동동문동
대산읍1.0000.9160.986
석남동0.9161.0000.922
동문동0.9860.9221.000

Missing values

2024-01-10T04:45:26.657570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:45:26.726861image/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.
2024-01-10T04:45:26.798383image/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

품목단위대산읍석남동동문동
0설렁탕1그릇800060008000
1냉면1그릇600060006000
2비빔밥1인분500050005000
3갈비탕1그릇8000150008000
4삼계탕1그릇100001200012000
5김치찌개 백반1인분600060007000
6된장찌개 백반1인분600060007000
7불고기(한우)200g120001000013000
8등심구이(한우)200g250003000032000
9돼지갈비200g130001400013000
품목단위대산읍석남동동문동
33PC방이용료1시간1500<NA>1000
34영화관람료성인 1회<NA><NA>10000
35사진촬영료3*4cm 1조17000<NA>20000
36사진인화료10.2*15.2cm 1장300<NA>300
37숙박료(모텔)1일40000<NA>30000
38이용료성인 1회120001500012000
39미용료성인 1회(파마)500006000040000
40미용료성인 1회(드라이)13000800012000
41목욕료성인 1회500060006000
42찜질방이용료성인 1회800080007000