Overview

Dataset statistics

Number of variables6
Number of observations45
Missing cells40
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory54.9 B

Variable types

Numeric4
Text1
Categorical1

Dataset

Description전라북도 임실군의 개인서비스요금 데이터 입니다. 데이터 세부내역에는 순번, 품목, 조사기준, 요금를 포함하여 데이터를 제공하고 있습니다.
Author전라북도 임실군
URLhttps://www.data.go.kr/data/15055160/fileData.do

Alerts

요금(임실읍) is highly overall correlated with 요금(오수면) and 2 other fieldsHigh correlation
요금(오수면) is highly overall correlated with 요금(임실읍) and 2 other fieldsHigh correlation
요금(관촌면) is highly overall correlated with 요금(임실읍) and 1 other fieldsHigh correlation
조사기준 is highly overall correlated with 요금(임실읍) and 1 other fieldsHigh correlation
요금(임실읍) has 9 (20.0%) missing valuesMissing
요금(오수면) has 15 (33.3%) missing valuesMissing
요금(관촌면) has 16 (35.6%) missing valuesMissing
순번 has unique valuesUnique
품목 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:33:55.834357
Analysis finished2023-12-12 09:33:58.576059
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T18:33:58.649641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q112
median23
Q334
95-th percentile42.8
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.133926
Coefficient of variation (CV)0.57104024
Kurtosis-1.2
Mean23
Median Absolute Deviation (MAD)11
Skewness0
Sum1035
Variance172.5
MonotonicityStrictly increasing
2023-12-12T18:33:58.784120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 1
 
2.2%
35 1
 
2.2%
26 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%
36 1
2.2%

품목
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-12T18:33:59.037206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length4.8
Min length2

Characters and Unicode

Total characters216
Distinct characters108
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

Unique45 ?
Unique (%)100.0%

Sample

1st row설렁탕
2nd row냉 면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
설렁탕 1
 
2.1%
볼링장이용료 1
 
2.1%
미용료(커트 1
 
2.1%
목욕료 1
 
2.1%
숙박료(호텔 1
 
2.1%
숙박료(여관 1
 
2.1%
세탁료 1
 
2.1%
공동주택관리비 1
 
2.1%
운동경기관람료 1
 
2.1%
영화관람료 1
 
2.1%
Other values (37) 37
78.7%
2023-12-12T18:33:59.464348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 20
 
9.3%
이 12
 
5.6%
용 11
 
5.1%
( 7
 
3.2%
) 7
 
3.2%
장 6
 
2.8%
수 4
 
1.9%
탕 4
 
1.9%
비 4
 
1.9%
관 4
 
1.9%
Other values (98) 137
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 197
91.2%
Open Punctuation 7
 
3.2%
Close Punctuation 7
 
3.2%
Space Separator 3
 
1.4%
Uppercase Letter 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 20
 
10.2%
이 12
 
6.1%
용 11
 
5.6%
장 6
 
3.0%
수 4
 
2.0%
탕 4
 
2.0%
비 4
 
2.0%
관 4
 
2.0%
면 3
 
1.5%
밥 3
 
1.5%
Other values (93) 126
64.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 197
91.2%
Common 17
 
7.9%
Latin 2
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 20
 
10.2%
이 12
 
6.1%
용 11
 
5.6%
장 6
 
3.0%
수 4
 
2.0%
탕 4
 
2.0%
비 4
 
2.0%
관 4
 
2.0%
면 3
 
1.5%
밥 3
 
1.5%
Other values (93) 126
64.0%
Common
ValueCountFrequency (%)
( 7
41.2%
) 7
41.2%
3
17.6%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 197
91.2%
ASCII 19
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 20
 
10.2%
이 12
 
6.1%
용 11
 
5.6%
장 6
 
3.0%
수 4
 
2.0%
탕 4
 
2.0%
비 4
 
2.0%
관 4
 
2.0%
면 3
 
1.5%
밥 3
 
1.5%
Other values (93) 126
64.0%
ASCII
ValueCountFrequency (%)
( 7
36.8%
) 7
36.8%
3
15.8%
C 1
 
5.3%
P 1
 
5.3%

조사기준
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
1인분
16 
1인 성인기준
6 
1시간
3 
1인분/200g
3 
1회
2 
Other values (14)
15 

Length

Max length10
Median length8
Mean length4.1777778
Min length1

Unique

Unique13 ?
Unique (%)28.9%

Sample

1st row1인분
2nd row1인분
3rd row1인분
4th row1인분
5th row1인분

Common Values

ValueCountFrequency (%)
1인분 16
35.6%
1인 성인기준 6
 
13.3%
1시간 3
 
6.7%
1인분/200g 3
 
6.7%
1회 2
 
4.4%
1개월(성인 남자) 2
 
4.4%
1박 1
 
2.2%
(대) 1
 
2.2%
1개 1
 
2.2%
한판(라지) 1
 
2.2%
Other values (9) 9
20.0%

Length

2023-12-12T18:33:59.591748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1인분 16
30.2%
성인기준 6
 
11.3%
1인 6
 
11.3%
1시간 3
 
5.7%
1인분/200g 3
 
5.7%
1회 2
 
3.8%
1개월(성인 2
 
3.8%
남자 2
 
3.8%
1게임 1
 
1.9%
1장 1
 
1.9%
Other values (11) 11
20.8%

요금(임실읍)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)52.8%
Missing9
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean10644.444
Minimum300
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T18:33:59.709481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1225
Q14000
median6000
Q311250
95-th percentile42500
Maximum58000
Range57700
Interquartile range (IQR)7250

Descriptive statistics

Standard deviation12920.868
Coefficient of variation (CV)1.2138602
Kurtosis6.7593324
Mean10644.444
Median Absolute Deviation (MAD)3250
Skewness2.6212259
Sum383200
Variance1.6694883 × 108
MonotonicityNot monotonic
2023-12-12T18:33:59.838508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6000 5
11.1%
5000 4
8.9%
7000 4
8.9%
10000 3
 
6.7%
12000 3
 
6.7%
4000 2
 
4.4%
3000 2
 
4.4%
20000 2
 
4.4%
16000 1
 
2.2%
1800 1
 
2.2%
Other values (9) 9
20.0%
(Missing) 9
20.0%
ValueCountFrequency (%)
300 1
 
2.2%
1000 1
 
2.2%
1300 1
 
2.2%
1800 1
 
2.2%
2500 1
 
2.2%
3000 2
 
4.4%
3300 1
 
2.2%
4000 2
 
4.4%
5000 4
8.9%
6000 5
11.1%
ValueCountFrequency (%)
58000 1
 
2.2%
50000 1
 
2.2%
40000 1
 
2.2%
20000 2
 
4.4%
16000 1
 
2.2%
12000 3
6.7%
11000 1
 
2.2%
10000 3
6.7%
7000 4
8.9%
6000 5
11.1%

요금(오수면)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)53.3%
Missing15
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean9955.3333
Minimum1200
Maximum46460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T18:33:59.975877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile2000
Q14250
median6000
Q310000
95-th percentile34550
Maximum46460
Range45260
Interquartile range (IQR)5750

Descriptive statistics

Standard deviation10897.187
Coefficient of variation (CV)1.0946079
Kurtosis4.4333522
Mean9955.3333
Median Absolute Deviation (MAD)2000
Skewness2.2232819
Sum298660
Variance1.1874868 × 108
MonotonicityNot monotonic
2023-12-12T18:34:00.083060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
5000 6
 
13.3%
6000 4
 
8.9%
4000 3
 
6.7%
7000 2
 
4.4%
3000 2
 
4.4%
2000 2
 
4.4%
10000 2
 
4.4%
34000 1
 
2.2%
16000 1
 
2.2%
11000 1
 
2.2%
Other values (6) 6
 
13.3%
(Missing) 15
33.3%
ValueCountFrequency (%)
1200 1
 
2.2%
2000 2
 
4.4%
3000 2
 
4.4%
4000 3
6.7%
5000 6
13.3%
6000 4
8.9%
7000 2
 
4.4%
8000 1
 
2.2%
10000 2
 
4.4%
11000 1
 
2.2%
ValueCountFrequency (%)
46460 1
2.2%
35000 1
2.2%
34000 1
2.2%
25000 1
2.2%
16000 1
2.2%
12000 1
2.2%
11000 1
2.2%
10000 2
4.4%
8000 1
2.2%
7000 2
4.4%

요금(관촌면)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)48.3%
Missing16
Missing (%)35.6%
Infinite0
Infinite (%)0.0%
Mean9151.7241
Minimum400
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T18:34:00.178745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile1400
Q15000
median6000
Q312000
95-th percentile26000
Maximum30000
Range29600
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation7714.3012
Coefficient of variation (CV)0.8429342
Kurtosis1.9616742
Mean9151.7241
Median Absolute Deviation (MAD)2000
Skewness1.5389983
Sum265400
Variance59510443
MonotonicityNot monotonic
2023-12-12T18:34:00.281727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5000 6
 
13.3%
6000 4
 
8.9%
12000 3
 
6.7%
7000 2
 
4.4%
3000 2
 
4.4%
18000 2
 
4.4%
8000 2
 
4.4%
30000 2
 
4.4%
16000 1
 
2.2%
2000 1
 
2.2%
Other values (4) 4
 
8.9%
(Missing) 16
35.6%
ValueCountFrequency (%)
400 1
 
2.2%
1000 1
 
2.2%
2000 1
 
2.2%
3000 2
 
4.4%
4000 1
 
2.2%
5000 6
13.3%
6000 4
8.9%
7000 2
 
4.4%
8000 2
 
4.4%
12000 3
6.7%
ValueCountFrequency (%)
30000 2
 
4.4%
20000 1
 
2.2%
18000 2
 
4.4%
16000 1
 
2.2%
12000 3
6.7%
8000 2
 
4.4%
7000 2
 
4.4%
6000 4
8.9%
5000 6
13.3%
4000 1
 
2.2%

Interactions

2023-12-12T18:33:57.471110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.125737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.582004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:57.054369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:57.572234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.217695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.712759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:57.158553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:58.073849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.340621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.815910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:57.274383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:58.167489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.457644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:56.923094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:33:57.363061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:34:00.364972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번품목조사기준요금(임실읍)요금(오수면)요금(관촌면)
순번1.0001.0000.8220.4930.1690.586
품목1.0001.0001.0001.0001.0001.000
조사기준0.8221.0001.0000.9090.8430.770
요금(임실읍)0.4931.0000.9091.0000.9450.867
요금(오수면)0.1691.0000.8430.9451.0000.820
요금(관촌면)0.5861.0000.7700.8670.8201.000
2023-12-12T18:34:00.492919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번요금(임실읍)요금(오수면)요금(관촌면)조사기준
순번1.000-0.118-0.0090.0280.397
요금(임실읍)-0.1181.0000.9690.9570.578
요금(오수면)-0.0090.9691.0000.9370.535
요금(관촌면)0.0280.9570.9371.0000.461
조사기준0.3970.5780.5350.4611.000

Missing values

2023-12-12T18:33:58.311050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:33:58.423015image/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.
2023-12-12T18:33:58.518121image/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

순번품목조사기준요금(임실읍)요금(오수면)요금(관촌면)
01설렁탕1인분7000<NA><NA>
12냉 면1인분700060006000
23비빔밥1인분600050005000
34갈비탕1인분700050007000
45삼계탕1인분120001100012000
56돼지불고기1인분1000070005000
67김치찌개백반1인분600060006000
78된장찌개백반1인분600050006000
89등심구이1인분/200g<NA>34000<NA>
910생선초밥1인분<NA><NA><NA>
순번품목조사기준요금(임실읍)요금(오수면)요금(관촌면)
3536골프연습장이용료1개월(성인 남자)<NA><NA><NA>
3637노래방이용료1시간20000<NA>20000
3738당구장이용료1시간130012001000
3839의복수선료<NA>300030003000
3940사진촬영료명함판120001200012000
4041사진인화료1장300<NA>400
4142콘도이용료1인 성인기준<NA><NA><NA>
4243PC방 이용료1시간1000<NA><NA>
4344택배이용료기본400040005000
4445찜질방이용료1인 성인기준<NA><NA><NA>