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

Numeric2
Categorical5
Text1

Dataset

Description샘플 데이터
Author성균관대학교 산학협력단
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=0d7f33b0-2fce-11ea-94b6-73a02796bba4

Alerts

연월일 has constant value ""Constant
환경플랫폼 하위 도메인명 has constant value ""Constant
도메인 하위 카테고리명 has constant value ""Constant
SNS 채널명 has constant value ""Constant
일간연관어연번 has unique valuesUnique
연관어명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:42:56.970055
Analysis finished2023-12-10 12:42:57.744784
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일간연관어연번
Real number (ℝ)

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
2023-12-10T21:42:57.840264image/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
2023-12-10T21:42:57.986734image/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
2020-01-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-01
2nd row2020-01-01
3rd row2020-01-01
4th row2020-01-01
5th row2020-01-01

Common Values

ValueCountFrequency (%)
2020-01-01 100
100.0%

Length

2023-12-10T21:42:58.104912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:58.187154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-01-01 100
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

2023-12-10T21:42:58.271618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:58.360470image/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

2023-12-10T21:42:58.473254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:58.571836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물재난 100
100.0%

SNS 채널명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
All 100
100.0%

Length

2023-12-10T21:42:58.725593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:58.810392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
all 100
100.0%

단어속성명
Categorical

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
속성
31 
라이프
27 
기타
16 
장소
8 
인물
8 
Other values (6)
10 

Length

Max length6
Median length2
Mean length2.34
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row장소
2nd row사회이슈
3rd row장소
4th row상품
5th row기타

Common Values

ValueCountFrequency (%)
속성 31
31.0%
라이프 27
27.0%
기타 16
16.0%
장소 8
 
8.0%
인물 8
 
8.0%
상품 4
 
4.0%
단체 2
 
2.0%
사회이슈 1
 
1.0%
엔터테인먼트 1
 
1.0%
시간 1
 
1.0%

Length

2023-12-10T21:42:58.918892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
속성 31
31.0%
라이프 27
27.0%
기타 16
16.0%
장소 8
 
8.0%
인물 8
 
8.0%
상품 4
 
4.0%
단체 2
 
2.0%
사회이슈 1
 
1.0%
엔터테인먼트 1
 
1.0%
시간 1
 
1.0%

연관어명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:59.239197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.52
Min length2

Characters and Unicode

Total characters252
Distinct characters129
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row4대강
2nd row4대강사업
3rd row가게
4th row가구
5th row가두다
ValueCountFrequency (%)
4대강 1
 
1.0%
군주 1
 
1.0%
기욤 1
 
1.0%
기억 1
 
1.0%
기아 1
 
1.0%
기상이변 1
 
1.0%
기상 1
 
1.0%
기둥 1
 
1.0%
급여 1
 
1.0%
글쓰기 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T21:42:59.756574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 16
 
6.3%
다 15
 
6.0%
가 13
 
5.2%
공 11
 
4.4%
구 8
 
3.2%
나 7
 
2.8%
경 6
 
2.4%
고 6
 
2.4%
김 5
 
2.0%
강 5
 
2.0%
Other values (119) 160
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
99.2%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 16
 
6.4%
다 15
 
6.0%
가 13
 
5.2%
공 11
 
4.4%
구 8
 
3.2%
나 7
 
2.8%
경 6
 
2.4%
고 6
 
2.4%
김 5
 
2.0%
강 5
 
2.0%
Other values (118) 158
63.2%
Decimal Number
ValueCountFrequency (%)
4 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 250
99.2%
Common 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 16
 
6.4%
다 15
 
6.0%
가 13
 
5.2%
공 11
 
4.4%
구 8
 
3.2%
나 7
 
2.8%
경 6
 
2.4%
고 6
 
2.4%
김 5
 
2.0%
강 5
 
2.0%
Other values (118) 158
63.2%
Common
ValueCountFrequency (%)
4 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
99.2%
ASCII 2
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 16
 
6.4%
다 15
 
6.0%
가 13
 
5.2%
공 11
 
4.4%
구 8
 
3.2%
나 7
 
2.8%
경 6
 
2.4%
고 6
 
2.4%
김 5
 
2.0%
강 5
 
2.0%
Other values (118) 158
63.2%
ASCII
ValueCountFrequency (%)
4 2
100.0%

일간연관어언급량
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:59.903130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2472191
Coefficient of variation (CV)0.89087081
Kurtosis27.648095
Mean1.4
Median Absolute Deviation (MAD)0
Skewness4.9025048
Sum140
Variance1.5555556
MonotonicityNot monotonic
2023-12-10T21:43:00.028990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 81
81.0%
2 12
 
12.0%
3 4
 
4.0%
7 1
 
1.0%
10 1
 
1.0%
6 1
 
1.0%
ValueCountFrequency (%)
1 81
81.0%
2 12
 
12.0%
3 4
 
4.0%
6 1
 
1.0%
7 1
 
1.0%
10 1
 
1.0%
ValueCountFrequency (%)
10 1
 
1.0%
7 1
 
1.0%
6 1
 
1.0%
3 4
 
4.0%
2 12
 
12.0%
1 81
81.0%

Interactions

2023-12-10T21:42:57.360483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:57.186198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:57.450993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:57.280553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:43:00.122568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일간연관어연번단어속성명연관어명일간연관어언급량
일간연관어연번1.0000.3161.0000.000
단어속성명0.3161.0001.0000.000
연관어명1.0001.0001.0001.000
일간연관어언급량0.0000.0001.0001.000
2023-12-10T21:43:00.214303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일간연관어연번일간연관어언급량단어속성명
일간연관어연번1.0000.0980.135
일간연관어언급량0.0981.0000.000
단어속성명0.1350.0001.000

Missing values

2023-12-10T21:42:57.568415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:42:57.696983image/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

일간연관어연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명단어속성명연관어명일간연관어언급량
012020-01-01물환경물재난All장소4대강1
122020-01-01물환경물재난All사회이슈4대강사업1
232020-01-01물환경물재난All장소가게1
342020-01-01물환경물재난All상품가구1
452020-01-01물환경물재난All기타가두다1
562020-01-01물환경물재난All라이프가로등1
672020-01-01물환경물재난All기타가리다1
782020-01-01물환경물재난All라이프가문1
892020-01-01물환경물재난All라이프가뭄1
9102020-01-01물환경물재난All기타가재도구2
일간연관어연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명단어속성명연관어명일간연관어언급량
90912020-01-01물환경물재난All기타끌어안다2
91922020-01-01물환경물재난All속성끝자락1
92932020-01-01물환경물재난All기타나가다1
93942020-01-01물환경물재난All기타나르다2
94952020-01-01물환경물재난All속성나무3
95962020-01-01물환경물재난All속성나미1
96972020-01-01물환경물재난All속성나오다3
97982020-01-01물환경물재난All장소나일강1
98992020-01-01물환경물재난All라이프낙서1
991002020-01-01물환경물재난All속성난리1