Overview

Dataset statistics

Number of variables12
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory102.3 B

Variable types

Categorical9
Numeric2
Text1

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO010BSICGISNACKS

Alerts

MT has constant value ""Constant
제과류 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
동서식품 포스트 has constant value ""Constant
201809 is highly overall correlated with 1 and 1 other fieldsHigh correlation
60 is highly overall correlated with 201809 and 1 other fieldsHigh correlation
1 is highly overall correlated with 201809 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 06:56:57.534434
Analysis finished2023-12-10 06:56:58.552490
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MT
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
MT
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MT 99
100.0%

Length

2023-12-10T15:56:58.628799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:56:58.712015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201809
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201809
59 
201801
40 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201809
2nd row201809
3rd row201809
4th row201809
5th row201809

Common Values

ValueCountFrequency (%)
201809 59
59.6%
201801 40
40.4%

Length

2023-12-10T15:56:58.803252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:56:58.895129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201809 59
59.6%
201801 40
40.4%

105
Real number (ℝ)

Distinct68
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.38384
Minimum24
Maximum264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:56:59.009083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q181
median100
Q3117.5
95-th percentile165.3
Maximum264
Range240
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation38.666736
Coefficient of variation (CV)0.37766445
Kurtosis3.7239562
Mean102.38384
Median Absolute Deviation (MAD)18
Skewness1.309922
Sum10136
Variance1495.1165
MonotonicityNot monotonic
2023-12-10T15:56:59.160378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 3
 
3.0%
102 3
 
3.0%
95 3
 
3.0%
147 3
 
3.0%
119 3
 
3.0%
103 3
 
3.0%
122 3
 
3.0%
96 3
 
3.0%
101 3
 
3.0%
91 2
 
2.0%
Other values (58) 70
70.7%
ValueCountFrequency (%)
24 1
1.0%
29 1
1.0%
34 1
1.0%
36 1
1.0%
52 2
2.0%
56 2
2.0%
57 1
1.0%
59 1
1.0%
60 1
1.0%
62 1
1.0%
ValueCountFrequency (%)
264 1
 
1.0%
227 1
 
1.0%
213 1
 
1.0%
201 1
 
1.0%
177 1
 
1.0%
164 1
 
1.0%
161 1
 
1.0%
147 3
3.0%
146 1
 
1.0%
142 1
 
1.0%

44
Real number (ℝ)

Distinct39
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.747475
Minimum3
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:56:59.295695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q123
median30
Q334.5
95-th percentile52.2
Maximum74
Range71
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation12.251067
Coefficient of variation (CV)0.3984414
Kurtosis2.1421732
Mean30.747475
Median Absolute Deviation (MAD)6
Skewness1.0206323
Sum3044
Variance150.08864
MonotonicityNot monotonic
2023-12-10T15:56:59.439797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
32 8
 
8.1%
33 8
 
8.1%
28 5
 
5.1%
29 5
 
5.1%
17 4
 
4.0%
24 4
 
4.0%
16 4
 
4.0%
31 4
 
4.0%
18 4
 
4.0%
22 3
 
3.0%
Other values (29) 50
50.5%
ValueCountFrequency (%)
3 1
 
1.0%
9 1
 
1.0%
12 2
2.0%
16 4
4.0%
17 4
4.0%
18 4
4.0%
19 2
2.0%
21 2
2.0%
22 3
3.0%
23 3
3.0%
ValueCountFrequency (%)
74 2
2.0%
58 1
1.0%
57 1
1.0%
54 1
1.0%
52 1
1.0%
51 1
1.0%
50 2
2.0%
49 1
1.0%
46 1
1.0%
44 2
2.0%

제과류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
제과류
99 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제과류 99
100.0%

Length

2023-12-10T15:56:59.580956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:56:59.682491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제과류 99
100.0%

전체
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 99
100.0%

Length

2023-12-10T15:56:59.780703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:56:59.875539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

전체.1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 99
100.0%

Length

2023-12-10T15:56:59.970645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:00.083756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
1
59 
2
40 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 59
59.6%
2 40
40.4%

Length

2023-12-10T15:57:00.168688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:00.259591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59
59.6%
2 40
40.4%

60
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
60
40 
20
37 
70
19 
30
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
60 40
40.4%
20 37
37.4%
70 19
19.2%
30 3
 
3.0%

Length

2023-12-10T15:57:00.351762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:00.443079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 40
40.4%
20 37
37.4%
70 19
19.2%
30 3
 
3.0%

서울특별시
Categorical

Distinct18
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
22 
서울특별시
17 
인천광역시
7 
부산광역시
7 
대구광역시
6 
Other values (13)
40 

Length

Max length7
Median length5
Mean length4.3131313
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 22
22.2%
서울특별시 17
17.2%
인천광역시 7
 
7.1%
부산광역시 7
 
7.1%
대구광역시 6
 
6.1%
광주광역시 6
 
6.1%
전라북도 6
 
6.1%
경상남도 5
 
5.1%
강원도 4
 
4.0%
경상북도 3
 
3.0%
Other values (8) 16
16.2%

Length

2023-12-10T15:57:00.550747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 22
22.2%
서울특별시 17
17.2%
인천광역시 7
 
7.1%
부산광역시 7
 
7.1%
대구광역시 6
 
6.1%
광주광역시 6
 
6.1%
전라북도 6
 
6.1%
경상남도 5
 
5.1%
강원도 4
 
4.0%
전라남도 3
 
3.0%
Other values (8) 16
16.2%

도봉구
Text

Distinct62
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:57:00.774932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7575758
Min length2

Characters and Unicode

Total characters273
Distinct characters65
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

Unique45 ?
Unique (%)45.5%

Sample

1st row양천구
2nd row서초구
3rd row강남구
4th row부산진구
5th row기장군
ValueCountFrequency (%)
전체 13
 
13.1%
중구 7
 
7.1%
북구 4
 
4.0%
서구 4
 
4.0%
광산구 2
 
2.0%
양천구 2
 
2.0%
광명시 2
 
2.0%
서초구 2
 
2.0%
광주시 2
 
2.0%
화성시 2
 
2.0%
Other values (52) 59
59.6%
2023-12-10T15:57:01.181197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 43
 
15.8%
시 41
 
15.0%
전 14
 
5.1%
체 13
 
4.8%
주 11
 
4.0%
산 10
 
3.7%
서 8
 
2.9%
양 7
 
2.6%
포 7
 
2.6%
중 7
 
2.6%
Other values (55) 112
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 273
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 43
 
15.8%
시 41
 
15.0%
전 14
 
5.1%
체 13
 
4.8%
주 11
 
4.0%
산 10
 
3.7%
서 8
 
2.9%
양 7
 
2.6%
포 7
 
2.6%
중 7
 
2.6%
Other values (55) 112
41.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 273
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 43
 
15.8%
시 41
 
15.0%
전 14
 
5.1%
체 13
 
4.8%
주 11
 
4.0%
산 10
 
3.7%
서 8
 
2.9%
양 7
 
2.6%
포 7
 
2.6%
중 7
 
2.6%
Other values (55) 112
41.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 273
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 43
 
15.8%
시 41
 
15.0%
전 14
 
5.1%
체 13
 
4.8%
주 11
 
4.0%
산 10
 
3.7%
서 8
 
2.9%
양 7
 
2.6%
포 7
 
2.6%
중 7
 
2.6%
Other values (55) 112
41.0%

동서식품 포스트
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
동서식품 포스트
99 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동서식품 포스트
2nd row동서식품 포스트
3rd row동서식품 포스트
4th row동서식품 포스트
5th row동서식품 포스트

Common Values

ValueCountFrequency (%)
동서식품 포스트 99
100.0%

Length

2023-12-10T15:57:01.303273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:57:01.396011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동서식품 99
50.0%
포스트 99
50.0%

Interactions

2023-12-10T15:56:58.085331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:56:57.894532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:56:58.190171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:56:57.971716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:57:01.458533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20180910544160서울특별시도봉구
2018091.0000.1130.1840.9991.0000.4220.814
1050.1131.0000.7090.1130.0000.0000.919
440.1840.7091.0000.1840.2070.0000.714
10.9990.1130.1841.0001.0000.4220.814
601.0000.0000.2071.0001.0000.2400.000
서울특별시0.4220.0000.0000.4220.2401.0000.684
도봉구0.8140.9190.7140.8140.0000.6841.000
2023-12-10T15:57:01.573277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201809서울특별시601
2018091.0000.3020.9900.979
서울특별시0.3021.0000.1150.302
600.9900.1151.0000.990
10.9790.3020.9901.000
2023-12-10T15:57:01.683846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10544201809160서울특별시
1051.0000.4100.1330.1330.0000.000
440.4101.0000.1750.1750.1270.000
2018090.1330.1751.0000.9790.9900.302
10.1330.1750.9791.0000.9900.302
600.0000.1270.9900.9901.0000.115
서울특별시0.0000.0000.3020.3020.1151.000

Missing values

2023-12-10T15:56:58.322316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:56:58.483905image/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

MT20180910544제과류전체전체.1160서울특별시도봉구동서식품 포스트
0MT20180913735제과류전체전체160서울특별시양천구동서식품 포스트
1MT20180910831제과류전체전체160서울특별시서초구동서식품 포스트
2MT2018099528제과류전체전체160서울특별시강남구동서식품 포스트
3MT2018097217제과류전체전체160부산광역시부산진구동서식품 포스트
4MT201809243제과류전체전체160부산광역시기장군동서식품 포스트
5MT2018098149제과류전체전체160대구광역시중구동서식품 포스트
6MT20180914644제과류전체전체160대구광역시북구동서식품 포스트
7MT20180910839제과류전체전체160대구광역시수성구동서식품 포스트
8MT20180910336제과류전체전체160인천광역시중구동서식품 포스트
9MT2018093616제과류전체전체160인천광역시남동구동서식품 포스트
MT20180910544제과류전체전체.1160서울특별시도봉구동서식품 포스트
89MT2018018131제과류전체전체220전라남도전체동서식품 포스트
90MT2018019633제과류전체전체220전라남도목포시동서식품 포스트
91MT20180117751제과류전체전체220경상북도영주시동서식품 포스트
92MT2018015916제과류전체전체220경상북도칠곡군동서식품 포스트
93MT2018019935제과류전체전체220경상남도김해시동서식품 포스트
94MT20180111450제과류전체전체220경상남도거제시동서식품 포스트
95MT2018019528제과류전체전체220경상남도양산시동서식품 포스트
96MT20180113318제과류전체전체230서울특별시중구동서식품 포스트
97MT20180110230제과류전체전체230서울특별시용산구동서식품 포스트
98MT2018018023제과류전체전체230서울특별시성북구동서식품 포스트