Overview

Dataset statistics

Number of variables8
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory71.5 B

Variable types

Text1
DateTime2
Numeric5

Dataset

Description제주특별자치도에서 조사하여 발표하는 월간 개인서비스 요금과 관련한 데이터로 행정시별 각종 서비스 요금에 대한 가격 정보를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15010336/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
제주시 광양로터리 is highly overall correlated with 제주시 신제주 연동 and 3 other fieldsHigh correlation
제주시 신제주 연동 is highly overall correlated with 제주시 광양로터리 and 3 other fieldsHigh correlation
제주시 중앙로 is highly overall correlated with 제주시 광양로터리 and 3 other fieldsHigh correlation
서귀포시 동명백화점 is highly overall correlated with 제주시 광양로터리 and 3 other fieldsHigh correlation
서귀포시 중문동 is highly overall correlated with 제주시 광양로터리 and 3 other fieldsHigh correlation
제주시 중앙로 has 2 (3.8%) zerosZeros
서귀포시 중문동 has 2 (3.8%) zerosZeros

Reproduction

Analysis started2023-12-12 19:13:04.851121
Analysis finished2023-12-12 19:13:07.990609
Duration3.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

항목조사기준
Text

Distinct26
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-13T04:13:08.186098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length16.5
Mean length7.8461538
Min length3

Characters and Unicode

Total characters408
Distinct characters110
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPC방이용료
2nd rowPC방이용료
3rd row갈비탕
4th row갈비탕
5th row골프연습장이용료
ValueCountFrequency (%)
pc방이용료 2
 
3.3%
갈비탕 2
 
3.3%
세탁료(양복1벌 2
 
3.3%
설렁탕 2
 
3.3%
생선초밥 2
 
3.3%
삼계탕 2
 
3.3%
사진촬영료 2
 
3.3%
사진인화료 2
 
3.3%
비빔밥 2
 
3.3%
비빔냉면 2
 
3.3%
Other values (20) 40
66.7%
2023-12-13T04:13:08.659224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
료 20
 
4.9%
( 20
 
4.9%
) 20
 
4.9%
/ 12
 
2.9%
용 12
 
2.9%
외 10
 
2.5%
이 10
 
2.5%
비 10
 
2.5%
0 10
 
2.5%
1 8
 
2.0%
Other values (100) 276
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 300
73.5%
Decimal Number 34
 
8.3%
Open Punctuation 20
 
4.9%
Close Punctuation 20
 
4.9%
Other Punctuation 14
 
3.4%
Space Separator 8
 
2.0%
Lowercase Letter 6
 
1.5%
Uppercase Letter 4
 
1.0%
Other Symbol 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
료 20
 
6.7%
용 12
 
4.0%
외 10
 
3.3%
이 10
 
3.3%
비 10
 
3.3%
장 8
 
2.7%
고 6
 
2.0%
반 6
 
2.0%
백 6
 
2.0%
기 6
 
2.0%
Other values (83) 206
68.7%
Decimal Number
ValueCountFrequency (%)
0 10
29.4%
1 8
23.5%
8 4
 
11.8%
2 4
 
11.8%
3 2
 
5.9%
5 2
 
5.9%
4 2
 
5.9%
6 2
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 12
85.7%
. 2
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 2
50.0%
C 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 6
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 300
73.5%
Common 98
 
24.0%
Latin 10
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
료 20
 
6.7%
용 12
 
4.0%
외 10
 
3.3%
이 10
 
3.3%
비 10
 
3.3%
장 8
 
2.7%
고 6
 
2.0%
반 6
 
2.0%
백 6
 
2.0%
기 6
 
2.0%
Other values (83) 206
68.7%
Common
ValueCountFrequency (%)
( 20
20.4%
) 20
20.4%
/ 12
12.2%
0 10
10.2%
1 8
 
8.2%
8
 
8.2%
8 4
 
4.1%
2 4
 
4.1%
3 2
 
2.0%
㎡ 2
 
2.0%
Other values (4) 8
 
8.2%
Latin
ValueCountFrequency (%)
g 6
60.0%
P 2
 
20.0%
C 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 300
73.5%
ASCII 106
 
26.0%
CJK Compat 2
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
료 20
 
6.7%
용 12
 
4.0%
외 10
 
3.3%
이 10
 
3.3%
비 10
 
3.3%
장 8
 
2.7%
고 6
 
2.0%
반 6
 
2.0%
백 6
 
2.0%
기 6
 
2.0%
Other values (83) 206
68.7%
ASCII
ValueCountFrequency (%)
( 20
18.9%
) 20
18.9%
/ 12
11.3%
0 10
9.4%
1 8
 
7.5%
8
 
7.5%
g 6
 
5.7%
8 4
 
3.8%
2 4
 
3.8%
P 2
 
1.9%
Other values (6) 12
11.3%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%

해당연월
Date

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum2021-04-01 00:00:00
Maximum2021-05-01 00:00:00
2023-12-13T04:13:08.811647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:08.951660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

제주시 광양로터리
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15467.769
Minimum300
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:13:09.087092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1000
Q14600
median8000
Q313000
95-th percentile72912
Maximum100000
Range99700
Interquartile range (IQR)8400

Descriptive statistics

Standard deviation23059.084
Coefficient of variation (CV)1.4907828
Kurtosis6.7515336
Mean15467.769
Median Absolute Deviation (MAD)4000
Skewness2.7061397
Sum804324
Variance5.3172135 × 108
MonotonicityNot monotonic
2023-12-13T04:13:09.226151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8000 8
15.4%
5000 4
 
7.7%
12000 4
 
7.7%
15000 4
 
7.7%
10000 4
 
7.7%
50000 2
 
3.8%
20000 2
 
3.8%
300 2
 
3.8%
7000 2
 
3.8%
6000 2
 
3.8%
Other values (10) 18
34.6%
ValueCountFrequency (%)
300 2
3.8%
1000 2
3.8%
1800 2
3.8%
2000 2
3.8%
3500 2
3.8%
4000 2
3.8%
4600 2
3.8%
5000 4
7.7%
6000 2
3.8%
7000 2
3.8%
ValueCountFrequency (%)
100000 2
 
3.8%
73462 1
 
1.9%
72462 1
 
1.9%
50000 2
 
3.8%
20000 2
 
3.8%
15000 4
7.7%
13000 2
 
3.8%
12000 4
7.7%
10000 4
7.7%
8000 8
15.4%

제주시 신제주 연동
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17823.481
Minimum300
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:13:09.361172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1000
Q14600
median8250
Q315000
95-th percentile74465.45
Maximum120000
Range119700
Interquartile range (IQR)10400

Descriptive statistics

Standard deviation27324.312
Coefficient of variation (CV)1.5330514
Kurtosis6.8835368
Mean17823.481
Median Absolute Deviation (MAD)4600
Skewness2.7108766
Sum926821
Variance7.4661802 × 108
MonotonicityNot monotonic
2023-12-13T04:13:09.505478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8000 6
 
11.5%
15000 6
 
11.5%
3500 4
 
7.7%
10000 4
 
7.7%
7000 4
 
7.7%
1000 2
 
3.8%
70000 2
 
3.8%
9000 2
 
3.8%
12000 2
 
3.8%
300 2
 
3.8%
Other values (10) 18
34.6%
ValueCountFrequency (%)
300 2
 
3.8%
1000 2
 
3.8%
1700 2
 
3.8%
3500 4
7.7%
3800 2
 
3.8%
4600 2
 
3.8%
6000 2
 
3.8%
7000 4
7.7%
8000 6
11.5%
8500 2
 
3.8%
ValueCountFrequency (%)
120000 2
 
3.8%
74961 1
 
1.9%
74060 1
 
1.9%
70000 2
 
3.8%
22000 2
 
3.8%
20000 2
 
3.8%
15000 6
11.5%
12000 2
 
3.8%
10000 4
7.7%
9000 2
 
3.8%

제주시 중앙로
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15896.808
Minimum0
Maximum100000
Zeros2
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:13:09.633192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500
Q15000
median8000
Q314000
95-th percentile77067
Maximum100000
Range100000
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation23416.603
Coefficient of variation (CV)1.4730381
Kurtosis6.4582946
Mean15896.808
Median Absolute Deviation (MAD)3800
Skewness2.6712178
Sum826634
Variance5.4833729 × 108
MonotonicityNot monotonic
2023-12-13T04:13:09.776558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6000 8
 
15.4%
8000 6
 
11.5%
9000 4
 
7.7%
0 2
 
3.8%
5000 2
 
3.8%
50000 2
 
3.8%
13000 2
 
3.8%
10000 2
 
3.8%
20000 2
 
3.8%
500 2
 
3.8%
Other values (11) 20
38.5%
ValueCountFrequency (%)
0 2
 
3.8%
500 2
 
3.8%
2000 2
 
3.8%
3500 2
 
3.8%
3800 2
 
3.8%
4600 2
 
3.8%
5000 2
 
3.8%
6000 8
15.4%
8000 6
11.5%
9000 4
7.7%
ValueCountFrequency (%)
100000 2
3.8%
77617 1
1.9%
76617 1
1.9%
50000 2
3.8%
20000 2
3.8%
18000 2
3.8%
15000 2
3.8%
14000 2
3.8%
13000 2
3.8%
10800 2
3.8%

서귀포시 동명백화점
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11913
Minimum300
Maximum65338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:13:09.901573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1000
Q14600
median8000
Q315000
95-th percentile40000
Maximum65338
Range65038
Interquartile range (IQR)10400

Descriptive statistics

Standard deviation13335.263
Coefficient of variation (CV)1.1193875
Kurtosis9.1439816
Mean11913
Median Absolute Deviation (MAD)4000
Skewness2.8979093
Sum619476
Variance1.7782923 × 108
MonotonicityNot monotonic
2023-12-13T04:13:10.442309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
8000 12
23.1%
15000 8
15.4%
4000 4
 
7.7%
12000 2
 
3.8%
40000 2
 
3.8%
14000 2
 
3.8%
300 2
 
3.8%
9000 2
 
3.8%
6000 2
 
3.8%
1000 2
 
3.8%
Other values (7) 14
26.9%
ValueCountFrequency (%)
300 2
 
3.8%
1000 2
 
3.8%
1500 2
 
3.8%
3000 2
 
3.8%
4000 4
 
7.7%
4600 2
 
3.8%
6000 2
 
3.8%
7000 2
 
3.8%
8000 12
23.1%
9000 2
 
3.8%
ValueCountFrequency (%)
65338 2
 
3.8%
40000 2
 
3.8%
20000 2
 
3.8%
15000 8
15.4%
14000 2
 
3.8%
12000 2
 
3.8%
10000 2
 
3.8%
9000 2
 
3.8%
8000 12
23.1%
7000 2
 
3.8%

서귀포시 중문동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11653.115
Minimum0
Maximum67581
Zeros2
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T04:13:10.591133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300
Q14600
median9000
Q312000
95-th percentile36000
Maximum67581
Range67581
Interquartile range (IQR)7400

Descriptive statistics

Standard deviation13388.154
Coefficient of variation (CV)1.1488905
Kurtosis10.363961
Mean11653.115
Median Absolute Deviation (MAD)4200
Skewness3.0389999
Sum605962
Variance1.7924267 × 108
MonotonicityNot monotonic
2023-12-13T04:13:10.761533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
9000 6
 
11.5%
10000 6
 
11.5%
15000 4
 
7.7%
12000 4
 
7.7%
8000 4
 
7.7%
36000 2
 
3.8%
300 2
 
3.8%
4500 2
 
3.8%
18000 2
 
3.8%
5000 2
 
3.8%
Other values (10) 18
34.6%
ValueCountFrequency (%)
0 2
3.8%
300 2
3.8%
1500 2
3.8%
2500 2
3.8%
3500 2
3.8%
4500 2
3.8%
4600 2
3.8%
5000 2
3.8%
6000 2
3.8%
7000 2
3.8%
ValueCountFrequency (%)
67581 1
 
1.9%
66581 1
 
1.9%
36000 2
 
3.8%
20000 2
 
3.8%
18000 2
 
3.8%
15000 4
7.7%
12000 4
7.7%
10000 6
11.5%
9000 6
11.5%
8000 4
7.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum2021-05-27 00:00:00
Maximum2021-05-27 00:00:00
2023-12-13T04:13:10.915918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:11.050469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T04:13:07.192214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.124959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.578870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.122179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.623678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:07.312941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.201582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.679003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.211946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.752156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:07.430200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.285339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.804359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.312033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.874295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:07.518880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.376450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.893683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.402457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.972550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:07.620949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:05.471267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.025249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:06.494493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:13:07.085634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:13:11.160190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항목조사기준해당연월제주시 광양로터리제주시 신제주 연동제주시 중앙로서귀포시 동명백화점서귀포시 중문동
항목조사기준1.0000.0001.0001.0001.0001.0001.000
해당연월0.0001.0000.0000.0000.0000.0000.000
제주시 광양로터리1.0000.0001.0000.9970.9950.9690.979
제주시 신제주 연동1.0000.0000.9971.0001.0000.9510.981
제주시 중앙로1.0000.0000.9951.0001.0000.9390.977
서귀포시 동명백화점1.0000.0000.9690.9510.9391.0000.938
서귀포시 중문동1.0000.0000.9790.9810.9770.9381.000
2023-12-13T04:13:11.330270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제주시 광양로터리제주시 신제주 연동제주시 중앙로서귀포시 동명백화점서귀포시 중문동
제주시 광양로터리1.0000.9390.8620.9610.958
제주시 신제주 연동0.9391.0000.8820.9690.927
제주시 중앙로0.8620.8821.0000.8640.826
서귀포시 동명백화점0.9610.9690.8641.0000.969
서귀포시 중문동0.9580.9270.8260.9691.000

Missing values

2023-12-13T04:13:07.770408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:13:07.922439image/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

항목조사기준해당연월제주시 광양로터리제주시 신제주 연동제주시 중앙로서귀포시 동명백화점서귀포시 중문동데이터기준일자
0PC방이용료2021-05100010000100002021-05-27
1PC방이용료2021-04100010000100002021-05-27
2갈비탕2021-051000010000600012000100002021-05-27
3갈비탕2021-041000010000600012000100002021-05-27
4골프연습장이용료2021-0510000012000010000020000200002021-05-27
5골프연습장이용료2021-0410000012000010000020000200002021-05-27
6공동주택관리비(82.645㎡)/3월부터 지역별 임의아파트 소수군 평균/2021-0572462740607661765338665812021-05-27
7공동주택관리비(82.645㎡)/3월부터 지역별 임의아파트 소수군 평균/2021-0473462749617761765338675812021-05-27
8국산차(녹차)2021-05460046004600460046002021-05-27
9국산차(녹차)2021-04460046004600460046002021-05-27
항목조사기준해당연월제주시 광양로터리제주시 신제주 연동제주시 중앙로서귀포시 동명백화점서귀포시 중문동데이터기준일자
42삼계탕2021-0512000120001000014000120002021-05-27
43삼계탕2021-0412000120001000014000120002021-05-27
44생선초밥2021-0515000150001300015000150002021-05-27
45생선초밥2021-0415000150001300015000150002021-05-27
46설렁탕2021-058000800080008000100002021-05-27
47설렁탕2021-048000800080008000100002021-05-27
48세탁료(양복1벌)2021-05800090009000800090002021-05-27
49세탁료(양복1벌)2021-04800090009000800090002021-05-27
50쇠고기(180g내외)/외식(등심)2021-0550000700005000040000360002021-05-27
51쇠고기(180g내외)/외식(등심)2021-0450000700005000040000360002021-05-27