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
DateTime1
Categorical4
Text1

Dataset

Description샘플 데이터
Author성균관대학교 산학협력단
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=3f9648c0-2fd3-11ea-94b6-73a02796bba4

Alerts

연월일 has constant value ""Constant
환경플랫폼 하위 도메인명 has constant value ""Constant
SNS 채널명 has constant value ""Constant
일간감성어연번 is highly overall correlated with 도메인 하위 카테고리명High correlation
도메인 하위 카테고리명 is highly overall correlated with 일간감성어연번High correlation
일간감성어연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:07:18.405100
Analysis finished2023-12-10 12:07:19.493495
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일간감성어연번
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
2023-12-10T21:07:19.583393image/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
2023-12-10T21:07:19.744328image/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
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-01-01 00:00:00
Maximum2020-01-01 00:00:00
2023-12-10T21:07:19.872522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:19.978887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T21:07:20.082379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:20.175429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물환경 100
100.0%

도메인 하위 카테고리명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
물재난
73 
상수도
13 
하수도
8 
지하수
 
6

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 73
73.0%
상수도 13
 
13.0%
하수도 8
 
8.0%
지하수 6
 
6.0%

Length

2023-12-10T21:07:20.269924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:20.385920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물재난 73
73.0%
상수도 13
 
13.0%
하수도 8
 
8.0%
지하수 6
 
6.0%

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

2023-12-10T21:07:20.484544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:20.565954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
all 100
100.0%

감성어명
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:07:20.830347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.67
Min length2

Characters and Unicode

Total characters267
Distinct characters136
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

Unique84 ?
Unique (%)84.0%

Sample

1st row가치
2nd row감동
3rd row감사
4th row강하다
5th row거부
ValueCountFrequency (%)
친구 3
 
3.0%
맛있다 3
 
3.0%
재밌다 2
 
2.0%
사랑 2
 
2.0%
용기 2
 
2.0%
신나다 2
 
2.0%
괜찮다 2
 
2.0%
파멸 1
 
1.0%
찌질 1
 
1.0%
지혜 1
 
1.0%
Other values (81) 81
81.0%
2023-12-10T21:07:21.473153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 45
 
16.9%
기 6
 
2.2%
부 5
 
1.9%
하 5
 
1.9%
사 5
 
1.9%
나 5
 
1.9%
지 4
 
1.5%
이 4
 
1.5%
정 4
 
1.5%
감 4
 
1.5%
Other values (126) 180
67.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 267
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 45
 
16.9%
기 6
 
2.2%
부 5
 
1.9%
하 5
 
1.9%
사 5
 
1.9%
나 5
 
1.9%
지 4
 
1.5%
이 4
 
1.5%
정 4
 
1.5%
감 4
 
1.5%
Other values (126) 180
67.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 267
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 45
 
16.9%
기 6
 
2.2%
부 5
 
1.9%
하 5
 
1.9%
사 5
 
1.9%
나 5
 
1.9%
지 4
 
1.5%
이 4
 
1.5%
정 4
 
1.5%
감 4
 
1.5%
Other values (126) 180
67.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 267
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 45
 
16.9%
기 6
 
2.2%
부 5
 
1.9%
하 5
 
1.9%
사 5
 
1.9%
나 5
 
1.9%
지 4
 
1.5%
이 4
 
1.5%
정 4
 
1.5%
감 4
 
1.5%
Other values (126) 180
67.4%

감성타입
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
p
64 
n
36 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowp
2nd rowp
3rd rowp
4th rowp
5th rown

Common Values

ValueCountFrequency (%)
p 64
64.0%
n 36
36.0%

Length

2023-12-10T21:07:21.667743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:07:21.807132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
p 64
64.0%
n 36
36.0%

일간감성어언급량
Real number (ℝ)

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.75
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:07:21.941358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.05
Maximum17
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1432299
Coefficient of variation (CV)1.2247028
Kurtosis30.694908
Mean1.75
Median Absolute Deviation (MAD)0
Skewness5.1192697
Sum175
Variance4.5934343
MonotonicityNot monotonic
2023-12-10T21:07:22.088288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 71
71.0%
2 18
 
18.0%
3 3
 
3.0%
4 3
 
3.0%
5 1
 
1.0%
17 1
 
1.0%
12 1
 
1.0%
7 1
 
1.0%
6 1
 
1.0%
ValueCountFrequency (%)
1 71
71.0%
2 18
 
18.0%
3 3
 
3.0%
4 3
 
3.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
12 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
17 1
 
1.0%
12 1
 
1.0%
7 1
 
1.0%
6 1
 
1.0%
5 1
 
1.0%
4 3
 
3.0%
3 3
 
3.0%
2 18
 
18.0%
1 71
71.0%

Interactions

2023-12-10T21:07:19.006760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:18.742253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:19.135365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:18.863979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:07:22.193996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일간감성어연번도메인 하위 카테고리명감성어명감성타입일간감성어언급량
일간감성어연번1.0000.8750.0000.2490.000
도메인 하위 카테고리명0.8751.0000.0000.1620.000
감성어명0.0000.0001.0001.0000.000
감성타입0.2490.1621.0001.0000.000
일간감성어언급량0.0000.0000.0000.0001.000
2023-12-10T21:07:22.357936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도메인 하위 카테고리명감성타입
도메인 하위 카테고리명1.0000.105
감성타입0.1051.000
2023-12-10T21:07:22.473576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일간감성어연번일간감성어언급량도메인 하위 카테고리명감성타입
일간감성어연번1.000-0.2470.7150.181
일간감성어언급량-0.2471.0000.0000.000
도메인 하위 카테고리명0.7150.0001.0000.105
감성타입0.1810.0000.1051.000

Missing values

2023-12-10T21:07:19.298824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:07:19.441942image/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

일간감성어연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명감성어명감성타입일간감성어언급량
012020-01-01물환경물재난All가치p1
122020-01-01물환경물재난All감동p2
232020-01-01물환경물재난All감사p3
342020-01-01물환경물재난All강하다p1
452020-01-01물환경물재난All거부n1
562020-01-01물환경물재난All거침없다p1
672020-01-01물환경물재난All걱정n4
782020-01-01물환경물재난All고통n1
892020-01-01물환경물재난All공포증n1
9102020-01-01물환경물재난All괜찮다p1
일간감성어연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명감성어명감성타입일간감성어언급량
90912020-01-01물환경지하수All재밌다p1
91922020-01-01물환경지하수All진실p1
92932020-01-01물환경하수도All곰팡이n1
93942020-01-01물환경하수도All나쁘다n1
94952020-01-01물환경하수도All말썽n1
95962020-01-01물환경하수도All맛있다p1
96972020-01-01물환경하수도All묘하다p1
97982020-01-01물환경하수도All비싸다n1
98992020-01-01물환경하수도All인기p1
991002020-01-01물환경하수도All친구p1