Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells9
Missing cells (%)1.1%
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=8a03ac90-2fc9-11ea-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
일간지역언급량연번 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 일간지역언급량연번High correlation
시군구명 has 9 (9.0%) missing valuesMissing
일간지역언급량연번 has unique valuesUnique
일간시도언급량 has 54 (54.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:45:58.455501
Analysis finished2023-12-10 13:45:59.863326
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일간지역언급량연번
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
2023-12-10T22:46:00.029808image/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
2023-12-10T22:46:00.364233image/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
2017-01-02
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-01-02
2nd row2017-01-02
3rd row2017-01-02
4th row2017-01-02
5th row2017-01-02

Common Values

ValueCountFrequency (%)
2017-01-02 100
100.0%

Length

2023-12-10T22:46:00.685162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:01.453837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-01-02 100
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

2023-12-10T22:46:01.718279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:01.915535image/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
하천
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
하천 100
100.0%

Length

2023-12-10T22:46:02.211151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:02.409830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천 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

2023-12-10T22:46:02.587778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:02.758486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
all 100
100.0%

시도명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
26 
경기
18 
부산
17 
인천
11 
대구
9 
Other values (4)
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 26
26.0%
경기 18
18.0%
부산 17
17.0%
인천 11
11.0%
대구 9
 
9.0%
광주 6
 
6.0%
대전 6
 
6.0%
울산 6
 
6.0%
세종 1
 
1.0%

Length

2023-12-10T22:46:02.945362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:03.187325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 26
26.0%
경기 18
18.0%
부산 17
17.0%
인천 11
11.0%
대구 9
 
9.0%
광주 6
 
6.0%
대전 6
 
6.0%
울산 6
 
6.0%
세종 1
 
1.0%

시군구명
Text

MISSING 

Distinct70
Distinct (%)76.9%
Missing9
Missing (%)9.0%
Memory size932.0 B
2023-12-10T22:46:03.641078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8131868
Min length2

Characters and Unicode

Total characters256
Distinct characters74
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

Unique64 ?
Unique (%)70.3%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 6
 
6.6%
동구 6
 
6.6%
서구 5
 
5.5%
북구 4
 
4.4%
남구 4
 
4.4%
강서구 2
 
2.2%
울주군 1
 
1.1%
달서구 1
 
1.1%
달성군 1
 
1.1%
미추홀구 1
 
1.1%
Other values (60) 60
65.9%
2023-12-10T22:46:04.448823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 79
30.9%
동 14
 
5.5%
서 10
 
3.9%
중 8
 
3.1%
시 8
 
3.1%
남 7
 
2.7%
성 6
 
2.3%
북 6
 
2.3%
강 6
 
2.3%
군 5
 
2.0%
Other values (64) 107
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 79
30.9%
동 14
 
5.5%
서 10
 
3.9%
중 8
 
3.1%
시 8
 
3.1%
남 7
 
2.7%
성 6
 
2.3%
북 6
 
2.3%
강 6
 
2.3%
군 5
 
2.0%
Other values (64) 107
41.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 79
30.9%
동 14
 
5.5%
서 10
 
3.9%
중 8
 
3.1%
시 8
 
3.1%
남 7
 
2.7%
성 6
 
2.3%
북 6
 
2.3%
강 6
 
2.3%
군 5
 
2.0%
Other values (64) 107
41.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 79
30.9%
동 14
 
5.5%
서 10
 
3.9%
중 8
 
3.1%
시 8
 
3.1%
남 7
 
2.7%
성 6
 
2.3%
북 6
 
2.3%
강 6
 
2.3%
군 5
 
2.0%
Other values (64) 107
41.8%

일간시도언급량
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.55
Minimum0
Maximum7
Zeros54
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:46:04.675525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.275806
Coefficient of variation (CV)1.4682619
Kurtosis-0.022843826
Mean1.55
Median Absolute Deviation (MAD)0
Skewness1.2612354
Sum155
Variance5.1792929
MonotonicityNot monotonic
2023-12-10T22:46:04.890069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 54
54.0%
1 16
 
16.0%
6 12
 
12.0%
2 8
 
8.0%
5 6
 
6.0%
7 2
 
2.0%
4 1
 
1.0%
3 1
 
1.0%
ValueCountFrequency (%)
0 54
54.0%
1 16
 
16.0%
2 8
 
8.0%
3 1
 
1.0%
4 1
 
1.0%
5 6
 
6.0%
6 12
 
12.0%
7 2
 
2.0%
ValueCountFrequency (%)
7 2
 
2.0%
6 12
 
12.0%
5 6
 
6.0%
4 1
 
1.0%
3 1
 
1.0%
2 8
 
8.0%
1 16
 
16.0%
0 54
54.0%

Interactions

2023-12-10T22:45:58.996632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:58.715149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:59.182126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:45:58.849364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:46:05.050131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일간지역언급량연번시도명시군구명일간시도언급량
일간지역언급량연번1.0000.9040.0000.380
시도명0.9041.0000.0000.000
시군구명0.0000.0001.0001.000
일간시도언급량0.3800.0001.0001.000
2023-12-10T22:46:05.256259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일간지역언급량연번일간시도언급량시도명
일간지역언급량연번1.000-0.1560.704
일간시도언급량-0.1561.0000.000
시도명0.7040.0001.000

Missing values

2023-12-10T22:45:59.436128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:45:59.748181image/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

일간지역언급량연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명시도명시군구명일간시도언급량
012017-01-02물환경하천All서울<NA>0
122017-01-02물환경하천All서울종로구0
232017-01-02물환경하천All서울중구5
342017-01-02물환경하천All서울용산구7
452017-01-02물환경하천All서울성동구1
562017-01-02물환경하천All서울광진구0
672017-01-02물환경하천All서울동대문구0
782017-01-02물환경하천All서울중랑구1
892017-01-02물환경하천All서울성북구0
9102017-01-02물환경하천All서울강북구2
일간지역언급량연번연월일환경플랫폼 하위 도메인명도메인 하위 카테고리명SNS 채널명시도명시군구명일간시도언급량
90912017-01-02물환경하천All경기중원구0
91922017-01-02물환경하천All경기분당구0
92932017-01-02물환경하천All경기의정부시0
93942017-01-02물환경하천All경기안양시0
94952017-01-02물환경하천All경기만안구0
95962017-01-02물환경하천All경기동안구0
96972017-01-02물환경하천All경기부천시0
97982017-01-02물환경하천All경기광명시3
98992017-01-02물환경하천All경기평택시0
991002017-01-02물환경하천All경기동두천시0