Overview

Dataset statistics

Number of variables4
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory34.3 B

Variable types

Categorical2
Text1
Numeric1

Alerts

fclty_co is highly overall correlated with lclas_nm and 1 other fieldsHigh correlation
lclas_nm is highly overall correlated with fclty_co and 1 other fieldsHigh correlation
mlsfc_nm is highly overall correlated with fclty_co and 1 other fieldsHigh correlation
lclas_nm is highly imbalanced (80.6%)Imbalance
mlsfc_nm is highly imbalanced (80.6%)Imbalance
signgu_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:57:19.598994
Analysis finished2023-12-10 09:57:20.746727
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

lclas_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
문화예술
97 
소비
 
3

Length

Max length4
Median length4
Mean length3.94
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row문화예술
2nd row소비
3rd row문화예술
4th row문화예술
5th row문화예술

Common Values

ValueCountFrequency (%)
문화예술 97
97.0%
소비 3
 
3.0%

Length

2023-12-10T18:57:20.946247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:21.174798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화예술 97
97.0%
소비 3
 
3.0%

mlsfc_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
공연시설
97 
기타소비
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공연시설
2nd row기타소비
3rd row공연시설
4th row공연시설
5th row공연시설

Common Values

ValueCountFrequency (%)
공연시설 97
97.0%
기타소비 3
 
3.0%

Length

2023-12-10T18:57:21.373034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:21.658562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공연시설 97
97.0%
기타소비 3
 
3.0%

signgu_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:57:22.191791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.46
Min length7

Characters and Unicode

Total characters846
Distinct characters87
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 row서울특별시 종로구
2nd row경상남도 합천군
3rd row서울특별시 용산구
4th row서울특별시 성동구
5th row서울특별시 광진구
ValueCountFrequency (%)
경기도 25
 
12.5%
서울특별시 23
 
11.5%
부산광역시 15
 
7.5%
인천광역시 10
 
5.0%
대구광역시 8
 
4.0%
동구 6
 
3.0%
대전광역시 5
 
2.5%
서구 5
 
2.5%
울산광역시 5
 
2.5%
광주광역시 5
 
2.5%
Other values (81) 93
46.5%
2023-12-10T18:57:23.188738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 101
 
11.9%
100
 
11.8%
구 76
 
9.0%
광 57
 
6.7%
역 48
 
5.7%
서 34
 
4.0%
도 30
 
3.5%
울 29
 
3.4%
별 27
 
3.2%
특 27
 
3.2%
Other values (77) 317
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 746
88.2%
Space Separator 100
 
11.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 101
 
13.5%
구 76
 
10.2%
광 57
 
7.6%
역 48
 
6.4%
서 34
 
4.6%
도 30
 
4.0%
울 29
 
3.9%
별 27
 
3.6%
특 27
 
3.6%
경 26
 
3.5%
Other values (76) 291
39.0%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 746
88.2%
Common 100
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 101
 
13.5%
구 76
 
10.2%
광 57
 
7.6%
역 48
 
6.4%
서 34
 
4.6%
도 30
 
4.0%
울 29
 
3.9%
별 27
 
3.6%
특 27
 
3.6%
경 26
 
3.5%
Other values (76) 291
39.0%
Common
ValueCountFrequency (%)
100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 746
88.2%
ASCII 100
 
11.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 101
 
13.5%
구 76
 
10.2%
광 57
 
7.6%
역 48
 
6.4%
서 34
 
4.6%
도 30
 
4.0%
울 29
 
3.9%
별 27
 
3.6%
특 27
 
3.6%
경 26
 
3.5%
Other values (76) 291
39.0%
ASCII
ValueCountFrequency (%)
100
100.0%

fclty_co
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.37
Minimum1
Maximum1374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:23.526288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q113
median22
Q344
95-th percentile268.2
Maximum1374
Range1373
Interquartile range (IQR)31

Descriptive statistics

Standard deviation171.75933
Coefficient of variation (CV)2.5122031
Kurtosis37.37799
Mean68.37
Median Absolute Deviation (MAD)13
Skewness5.6826505
Sum6837
Variance29501.266
MonotonicityNot monotonic
2023-12-10T18:57:23.905644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 5
 
5.0%
21 4
 
4.0%
17 4
 
4.0%
33 3
 
3.0%
14 3
 
3.0%
13 3
 
3.0%
37 3
 
3.0%
22 3
 
3.0%
11 3
 
3.0%
12 3
 
3.0%
Other values (48) 66
66.0%
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
3 2
2.0%
4 2
2.0%
5 2
2.0%
6 3
3.0%
7 2
2.0%
8 1
 
1.0%
9 3
3.0%
11 3
3.0%
ValueCountFrequency (%)
1374 1
1.0%
819 1
1.0%
507 1
1.0%
416 1
1.0%
329 1
1.0%
265 1
1.0%
178 1
1.0%
165 1
1.0%
149 1
1.0%
145 1
1.0%

Interactions

2023-12-10T18:57:19.915202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:57:24.563298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lclas_nmmlsfc_nmsigngu_nmfclty_co
lclas_nm1.0000.9631.0000.789
mlsfc_nm0.9631.0001.0000.789
signgu_nm1.0001.0001.0001.000
fclty_co0.7890.7891.0001.000
2023-12-10T18:57:24.802061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lclas_nmmlsfc_nm
lclas_nm1.0000.826
mlsfc_nm0.8261.000
2023-12-10T18:57:24.992558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_colclas_nmmlsfc_nm
fclty_co1.0000.5820.582
lclas_nm0.5821.0000.826
mlsfc_nm0.5820.8261.000

Missing values

2023-12-10T18:57:20.440691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:57:20.678159image/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

lclas_nmmlsfc_nmsigngu_nmfclty_co
0문화예술공연시설서울특별시 종로구329
1소비기타소비경상남도 합천군32
2문화예술공연시설서울특별시 용산구149
3문화예술공연시설서울특별시 성동구165
4문화예술공연시설서울특별시 광진구78
5문화예술공연시설서울특별시 동대문구21
6문화예술공연시설서울특별시 중랑구21
7소비기타소비제주특별자치도 제주시819
8문화예술공연시설서울특별시 강북구20
9문화예술공연시설서울특별시 도봉구16
lclas_nmmlsfc_nmsigngu_nmfclty_co
90문화예술공연시설경기도 군포시13
91문화예술공연시설경기도 의왕시4
92문화예술공연시설경기도 하남시25
93문화예술공연시설경기도 용인시137
94문화예술공연시설경기도 파주시14
95문화예술공연시설경기도 이천시23
96문화예술공연시설경기도 안성시19
97문화예술공연시설경기도 김포시38
98문화예술공연시설경기도 화성시40
99문화예술공연시설경기도 광주시33