Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells30
Missing cells (%)16.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory54.4 B

Variable types

Categorical3
Text1
Numeric1
Unsupported1

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://www.bigdata-region.kr/#/dataset/bea1fbc8-f22b-4aef-af53-54ae8a33b358

Alerts

기준년월 has constant value ""Constant
카드사명 has 30 (100.0%) missing valuesMissing
카드사명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
사용금액 has 3 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:44:11.617013
Analysis finished2023-12-10 13:44:12.808908
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2013-01
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013-01
2nd row2013-01
3rd row2013-01
4th row2013-01
5th row2013-01

Common Values

ValueCountFrequency (%)
2013-01 30
100.0%

Length

2023-12-10T22:44:12.969311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:13.142080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013-01 30
100.0%

시도명
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
20 
서울특별시
4 
인천광역시
3 
충청남도
 
2
충청북도
 
1

Length

Max length5
Median length3
Mean length3.5666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row경기도
2nd row경기도
3rd row경기도
4th row서울특별시
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 20
66.7%
서울특별시 4
 
13.3%
인천광역시 3
 
10.0%
충청남도 2
 
6.7%
충청북도 1
 
3.3%

Length

2023-12-10T22:44:13.366474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:13.586965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 20
66.7%
서울특별시 4
 
13.3%
인천광역시 3
 
10.0%
충청남도 2
 
6.7%
충청북도 1
 
3.3%

시군구명
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:44:13.944818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.1666667
Min length2

Characters and Unicode

Total characters125
Distinct characters46
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

Unique24 ?
Unique (%)80.0%

Sample

1st row구리시
2nd row부천시
3rd row성남시 중원구
4th row서초구
5th row의왕시
ValueCountFrequency (%)
서구 2
 
5.1%
서북구 2
 
5.1%
안산시 2
 
5.1%
상록구 2
 
5.1%
성남시 2
 
5.1%
천안시 2
 
5.1%
광명시 1
 
2.6%
용인시 1
 
2.6%
처인구 1
 
2.6%
마포구 1
 
2.6%
Other values (23) 23
59.0%
2023-12-10T22:44:14.495964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 21
16.8%
구 18
 
14.4%
9
 
7.2%
천 7
 
5.6%
안 6
 
4.8%
서 5
 
4.0%
성 3
 
2.4%
평 3
 
2.4%
군 3
 
2.4%
록 2
 
1.6%
Other values (36) 48
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116
92.8%
Space Separator 9
 
7.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
18.1%
구 18
 
15.5%
천 7
 
6.0%
안 6
 
5.2%
서 5
 
4.3%
성 3
 
2.6%
평 3
 
2.6%
군 3
 
2.6%
록 2
 
1.7%
수 2
 
1.7%
Other values (35) 46
39.7%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116
92.8%
Common 9
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
18.1%
구 18
 
15.5%
천 7
 
6.0%
안 6
 
5.2%
서 5
 
4.3%
성 3
 
2.6%
평 3
 
2.6%
군 3
 
2.6%
록 2
 
1.7%
수 2
 
1.7%
Other values (35) 46
39.7%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116
92.8%
ASCII 9
 
7.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 21
18.1%
구 18
 
15.5%
천 7
 
6.0%
안 6
 
5.2%
서 5
 
4.3%
성 3
 
2.6%
평 3
 
2.6%
군 3
 
2.6%
록 2
 
1.7%
수 2
 
1.7%
Other values (35) 46
39.7%
ASCII
ValueCountFrequency (%)
9
100.0%

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
15 
F
15 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 15
50.0%
F 15
50.0%

Length

2023-12-10T22:44:14.713426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:14.869733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 15
50.0%
f 15
50.0%

사용금액
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.262584 × 1010
Minimum0
Maximum4.9238198 × 1010
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:15.040666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q187340504
median6.8825036 × 109
Q32.0393871 × 1010
95-th percentile3.9252771 × 1010
Maximum4.9238198 × 1010
Range4.9238198 × 1010
Interquartile range (IQR)2.030653 × 1010

Descriptive statistics

Standard deviation1.4245654 × 1010
Coefficient of variation (CV)1.1282936
Kurtosis0.19135194
Mean1.262584 × 1010
Median Absolute Deviation (MAD)6.8803102 × 109
Skewness1.0480866
Sum3.7877521 × 1011
Variance2.0293867 × 1020
MonotonicityNot monotonic
2023-12-10T22:44:15.273116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 3
 
10.0%
14743106489 1
 
3.3%
30795343640 1
 
3.3%
61738299 1
 
3.3%
4386858 1
 
3.3%
23807742221 1
 
3.3%
428702479 1
 
3.3%
35471498878 1
 
3.3%
26418556501 1
 
3.3%
3722499422 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 3
10.0%
4386858 1
 
3.3%
26762337 1
 
3.3%
42599386 1
 
3.3%
61738299 1
 
3.3%
66572157 1
 
3.3%
149645546 1
 
3.3%
428702479 1
 
3.3%
2322762795 1
 
3.3%
3340303184 1
 
3.3%
ValueCountFrequency (%)
49238198016 1
3.3%
42346539837 1
3.3%
35471498878 1
3.3%
31627790828 1
3.3%
30795343640 1
3.3%
26418556501 1
3.3%
23807742221 1
3.3%
20895567760 1
3.3%
18888778916 1
3.3%
17978088522 1
3.3%

카드사명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

Interactions

2023-12-10T22:44:11.877888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:44:15.498858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명성별코드사용금액
시도명1.0001.0000.0000.000
시군구명1.0001.0000.0000.965
성별코드0.0000.0001.0000.288
사용금액0.0000.9650.2881.000
2023-12-10T22:44:15.650994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드시도명
성별코드1.0000.000
시도명0.0001.000
2023-12-10T22:44:15.794610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용금액시도명성별코드
사용금액1.0000.0000.157
시도명0.0001.0000.000
성별코드0.1570.0001.000

Missing values

2023-12-10T22:44:12.120161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:44:12.667502image/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

기준년월시도명시군구명성별코드사용금액카드사명
02013-01경기도구리시M14743106489<NA>
12013-01경기도부천시M49238198016<NA>
22013-01경기도성남시 중원구F16451955220<NA>
32013-01서울특별시서초구F26762337<NA>
42013-01경기도의왕시F7238056435<NA>
52013-01경기도성남시 수정구F8778660410<NA>
62013-01경기도안양시 동안구F31627790828<NA>
72013-01경기도양평군F2322762795<NA>
82013-01경기도이천시M20895567760<NA>
92013-01경기도파주시M42346539837<NA>
기준년월시도명시군구명성별코드사용금액카드사명
202013-01인천광역시서구M66572157<NA>
212013-01충청남도천안시 서북구F0<NA>
222013-01경기도과천시F3722499422<NA>
232013-01경기도광명시M26418556501<NA>
242013-01경기도수원시 권선구F35471498878<NA>
252013-01서울특별시구로구M428702479<NA>
262013-01경기도안산시 상록구M23807742221<NA>
272013-01서울특별시도봉구M4386858<NA>
282013-01인천광역시서구F61738299<NA>
292013-01충청북도음성군M0<NA>