Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory67.3 B

Variable types

Numeric1
Categorical6
Text1

Dataset

Description샘플 데이터
Author성균관대학교 산학협력단
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=d58abcc0-2fca-11ea-94b6-73a02796bba4

Alerts

연월일 has constant value ""Constant
환경플랫폼 하위 도메인명 has constant value ""Constant
도메인 하위 카테고리명 has constant value ""Constant
주간지역언급량연번 is highly overall correlated with SNS 채널명 and 1 other fieldsHigh correlation
SNS 채널명 is highly overall correlated with 주간지역언급량연번High correlation
시도명 is highly overall correlated with 주간지역언급량연번High correlation
주간지역언급량연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:09:41.457090
Analysis finished2023-12-10 11:09:42.245659
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주간지역언급량연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:09:42.368685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:09:42.581829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

연월일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-04
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-04
2nd row2021-01-04
3rd row2021-01-04
4th row2021-01-04
5th row2021-01-04

Common Values

ValueCountFrequency (%)
2021-01-04 100
100.0%

Length

2023-12-10T20:09:42.791195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:42.935112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-04 100
100.0%

환경플랫폼 하위 도메인명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
물환경
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
물환경 100
100.0%

Length

2023-12-10T20:09:43.083472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:43.233984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물환경 100
100.0%

도메인 하위 카테고리명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
물재난
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
물재난 100
100.0%

Length

2023-12-10T20:09:43.395795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:43.531337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물재난 100
100.0%

SNS 채널명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
All
68 
blog
32 

Length

Max length4
Median length3
Mean length3.32
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
All 68
68.0%
blog 32
32.0%

Length

2023-12-10T20:09:43.691478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:43.829436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
all 68
68.0%
blog 32
32.0%

시도명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
16 
서울
12 
경남
10 
대구
8 
부산
8 
Other values (11)
46 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원
2nd row강원
3rd row경기
4th row경기
5th row경기

Common Values

ValueCountFrequency (%)
경기 16
16.0%
서울 12
12.0%
경남 10
10.0%
대구 8
8.0%
부산 8
8.0%
광주 6
 
6.0%
대전 6
 
6.0%
전남 6
 
6.0%
전북 5
 
5.0%
충남 5
 
5.0%
Other values (6) 18
18.0%

Length

2023-12-10T20:09:43.974619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 16
16.0%
서울 12
12.0%
경남 10
10.0%
대구 8
8.0%
부산 8
8.0%
광주 6
 
6.0%
대전 6
 
6.0%
전남 6
 
6.0%
전북 5
 
5.0%
충남 5
 
5.0%
Other values (6) 18
18.0%

시군구명
Text

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:09:44.268800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.76
Min length2

Characters and Unicode

Total characters276
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)32.0%

Sample

1st row삼척시
2nd row인제군
3rd row고양시
4th row김포시
5th row남양주시
ValueCountFrequency (%)
동구 10
 
10.0%
서구 9
 
9.0%
북구 9
 
9.0%
달성군 2
 
2.0%
하동군 2
 
2.0%
포항시 2
 
2.0%
대덕구 2
 
2.0%
인제군 2
 
2.0%
마산 2
 
2.0%
진주시 2
 
2.0%
Other values (45) 58
58.0%
2023-12-10T20:09:44.858461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 46
16.7%
시 32
 
11.6%
군 21
 
7.6%
동 17
 
6.2%
서 11
 
4.0%
주 10
 
3.6%
북 10
 
3.6%
양 9
 
3.3%
포 6
 
2.2%
천 5
 
1.8%
Other values (51) 109
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 276
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 46
16.7%
시 32
 
11.6%
군 21
 
7.6%
동 17
 
6.2%
서 11
 
4.0%
주 10
 
3.6%
북 10
 
3.6%
양 9
 
3.3%
포 6
 
2.2%
천 5
 
1.8%
Other values (51) 109
39.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 276
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 46
16.7%
시 32
 
11.6%
군 21
 
7.6%
동 17
 
6.2%
서 11
 
4.0%
주 10
 
3.6%
북 10
 
3.6%
양 9
 
3.3%
포 6
 
2.2%
천 5
 
1.8%
Other values (51) 109
39.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 276
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 46
16.7%
시 32
 
11.6%
군 21
 
7.6%
동 17
 
6.2%
서 11
 
4.0%
주 10
 
3.6%
북 10
 
3.6%
양 9
 
3.3%
포 6
 
2.2%
천 5
 
1.8%
Other values (51) 109
39.5%

주간시도언급량
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
73 
2
17 
5
9 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 73
73.0%
2 17
 
17.0%
5 9
 
9.0%
3 1
 
1.0%

Length

2023-12-10T20:09:45.062175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:09:45.215777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 73
73.0%
2 17
 
17.0%
5 9
 
9.0%
3 1
 
1.0%

Interactions

2023-12-10T20:09:41.811660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:09:45.331305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주간지역언급량연번SNS 채널명시도명시군구명주간시도언급량
주간지역언급량연번1.0000.9980.9110.0000.141
SNS 채널명0.9981.0000.4260.0000.000
시도명0.9110.4261.0000.9420.000
시군구명0.0000.0000.9421.0001.000
주간시도언급량0.1410.0000.0001.0001.000
2023-12-10T20:09:45.478876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주간시도언급량시도명SNS 채널명
주간시도언급량1.0000.0000.000
시도명0.0001.0000.308
SNS 채널명0.0000.3081.000
2023-12-10T20:09:45.603949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주간지역언급량연번SNS 채널명시도명주간시도언급량
주간지역언급량연번1.0000.9190.6500.076
SNS 채널명0.9191.0000.3080.000
시도명0.6500.3081.0000.000
주간시도언급량0.0760.0000.0001.000

Missing values

2023-12-10T20:09:41.953518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:09:42.161315image/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

주간지역언급량연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명시도명시군구명주간시도언급량
012021-01-04물환경물재난All강원삼척시1
122021-01-04물환경물재난All강원인제군1
232021-01-04물환경물재난All경기고양시1
342021-01-04물환경물재난All경기김포시1
452021-01-04물환경물재난All경기남양주시2
562021-01-04물환경물재난All경기수원시1
672021-01-04물환경물재난All경기양주시1
782021-01-04물환경물재난All경기의정부시1
892021-01-04물환경물재난All경기평택시1
9102021-01-04물환경물재난All경기화성시1
주간지역언급량연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명시도명시군구명주간시도언급량
90912021-01-04물환경물재난blog대구북구1
91922021-01-04물환경물재난blog대구서구5
92932021-01-04물환경물재난blog대전대덕구1
93942021-01-04물환경물재난blog대전동구2
94952021-01-04물환경물재난blog대전서구5
95962021-01-04물환경물재난blog부산동구2
96972021-01-04물환경물재난blog부산북구1
97982021-01-04물환경물재난blog부산사하구1
98992021-01-04물환경물재난blog부산서구5
991002021-01-04물환경물재난blog서울강동구1