Overview

Dataset statistics

Number of variables13
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory112.3 B

Variable types

Categorical8
Numeric4
Text1

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO011TOTALCGIDIY

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
DIY용품 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
1 has constant value ""Constant
2010 is highly overall correlated with 3220 and 2 other fieldsHigh correlation
3220 is highly overall correlated with 2010 and 2 other fieldsHigh correlation
2010.1 is highly overall correlated with 2010 and 2 other fieldsHigh correlation
81 is highly overall correlated with 2010 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 06:45:20.444007
Analysis finished2023-12-10 06:45:23.205571
Duration2.76 seconds
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

2023-12-10T15:45:23.294195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:23.425290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201901
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201901
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201901
2nd row201901
3rd row201901
4th row201901
5th row201901

Common Values

ValueCountFrequency (%)
201901 99
100.0%

Length

2023-12-10T15:45:23.561995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:23.715884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

2010
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.79798
Minimum6
Maximum5511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:45:23.847057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28.8
Q162
median103
Q3202.5
95-th percentile445.7
Maximum5511
Range5505
Interquartile range (IQR)140.5

Descriptive statistics

Standard deviation564.68125
Coefficient of variation (CV)2.7044383
Kurtosis81.396079
Mean208.79798
Median Absolute Deviation (MAD)57
Skewness8.7008276
Sum20671
Variance318864.92
MonotonicityNot monotonic
2023-12-10T15:45:24.047061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 4
 
4.0%
64 2
 
2.0%
29 2
 
2.0%
46 2
 
2.0%
45 2
 
2.0%
102 2
 
2.0%
280 2
 
2.0%
170 2
 
2.0%
87 2
 
2.0%
56 2
 
2.0%
Other values (71) 77
77.8%
ValueCountFrequency (%)
6 1
1.0%
9 1
1.0%
10 1
1.0%
19 1
1.0%
27 1
1.0%
29 2
2.0%
33 1
1.0%
34 1
1.0%
37 1
1.0%
39 1
1.0%
ValueCountFrequency (%)
5511 1
1.0%
1279 1
1.0%
820 1
1.0%
560 1
1.0%
497 1
1.0%
440 1
1.0%
434 1
1.0%
339 1
1.0%
338 1
1.0%
328 1
1.0%

3220
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.22222
Minimum6
Maximum831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:45:24.247509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile18.9
Q147
median64
Q3110.5
95-th percentile251
Maximum831
Range825
Interquartile range (IQR)63.5

Descriptive statistics

Standard deviation126.41215
Coefficient of variation (CV)1.2246602
Kurtosis17.933903
Mean103.22222
Median Absolute Deviation (MAD)23
Skewness3.9710848
Sum10219
Variance15980.032
MonotonicityNot monotonic
2023-12-10T15:45:24.472731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 4
 
4.0%
80 3
 
3.0%
62 3
 
3.0%
143 2
 
2.0%
45 2
 
2.0%
43 2
 
2.0%
135 2
 
2.0%
46 2
 
2.0%
64 2
 
2.0%
59 2
 
2.0%
Other values (68) 75
75.8%
ValueCountFrequency (%)
6 1
1.0%
8 1
1.0%
9 1
1.0%
13 1
1.0%
18 1
1.0%
19 1
1.0%
22 1
1.0%
23 2
2.0%
30 1
1.0%
35 1
1.0%
ValueCountFrequency (%)
831 1
1.0%
721 1
1.0%
586 1
1.0%
431 1
1.0%
278 1
1.0%
248 1
1.0%
243 1
1.0%
204 1
1.0%
189 1
1.0%
177 1
1.0%

2010.1
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.79798
Minimum6
Maximum5511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:45:24.674822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile28.8
Q162
median103
Q3202.5
95-th percentile445.7
Maximum5511
Range5505
Interquartile range (IQR)140.5

Descriptive statistics

Standard deviation564.68125
Coefficient of variation (CV)2.7044383
Kurtosis81.396079
Mean208.79798
Median Absolute Deviation (MAD)57
Skewness8.7008276
Sum20671
Variance318864.92
MonotonicityNot monotonic
2023-12-10T15:45:25.270077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 4
 
4.0%
64 2
 
2.0%
29 2
 
2.0%
46 2
 
2.0%
45 2
 
2.0%
102 2
 
2.0%
280 2
 
2.0%
170 2
 
2.0%
87 2
 
2.0%
56 2
 
2.0%
Other values (71) 77
77.8%
ValueCountFrequency (%)
6 1
1.0%
9 1
1.0%
10 1
1.0%
19 1
1.0%
27 1
1.0%
29 2
2.0%
33 1
1.0%
34 1
1.0%
37 1
1.0%
39 1
1.0%
ValueCountFrequency (%)
5511 1
1.0%
1279 1
1.0%
820 1
1.0%
560 1
1.0%
497 1
1.0%
440 1
1.0%
434 1
1.0%
339 1
1.0%
338 1
1.0%
328 1
1.0%

DIY용품
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
DIY용품 99
100.0%

Length

2023-12-10T15:45:25.479545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:25.606006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
diy용품 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

2023-12-10T15:45:25.726616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:25.887234image/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

2023-12-10T15:45:26.016886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:26.154321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
1
99 

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 99
100.0%

Length

2023-12-10T15:45:26.298996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:26.445267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 99
100.0%

20
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
30
50 
20
42 
40
7 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
30 50
50.5%
20 42
42.4%
40 7
 
7.1%

Length

2023-12-10T15:45:26.592968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:45:26.745599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 50
50.5%
20 42
42.4%
40 7
 
7.1%

인천광역시
Categorical

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
서울특별시
16 
경기도
13 
인천광역시
11 
충청남도
11 
부산광역시
7 
Other values (12)
41 

Length

Max length7
Median length5
Mean length4.3939394
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row인천광역시
2nd row인천광역시
3rd row대전광역시
4th row울산광역시
5th row경기도

Common Values

ValueCountFrequency (%)
서울특별시 16
16.2%
경기도 13
13.1%
인천광역시 11
11.1%
충청남도 11
11.1%
부산광역시 7
7.1%
울산광역시 6
 
6.1%
전라남도 5
 
5.1%
대구광역시 5
 
5.1%
광주광역시 5
 
5.1%
강원도 4
 
4.0%
Other values (7) 16
16.2%

Length

2023-12-10T15:45:26.900174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 16
16.2%
경기도 13
13.1%
인천광역시 11
11.1%
충청남도 11
11.1%
부산광역시 7
7.1%
울산광역시 6
 
6.1%
대구광역시 5
 
5.1%
광주광역시 5
 
5.1%
전라남도 5
 
5.1%
강원도 4
 
4.0%
Other values (7) 16
16.2%

미추홀구
Text

Distinct55
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:45:27.185614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7979798
Min length2

Characters and Unicode

Total characters277
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

Unique26 ?
Unique (%)26.3%

Sample

1st row부평구
2nd row계양구
3rd row유성구
4th row남구
5th row동두천시
ValueCountFrequency (%)
전체 11
 
11.1%
북구 5
 
5.1%
서구 4
 
4.0%
중구 3
 
3.0%
서산시 2
 
2.0%
진주시 2
 
2.0%
광진구 2
 
2.0%
구로구 2
 
2.0%
관악구 2
 
2.0%
남동구 2
 
2.0%
Other values (45) 64
64.6%
2023-12-10T15:45:27.706368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 51
18.4%
시 36
 
13.0%
전 12
 
4.3%
체 11
 
4.0%
성 10
 
3.6%
산 10
 
3.6%
주 10
 
3.6%
서 8
 
2.9%
군 7
 
2.5%
진 7
 
2.5%
Other values (55) 115
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 277
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 51
18.4%
시 36
 
13.0%
전 12
 
4.3%
체 11
 
4.0%
성 10
 
3.6%
산 10
 
3.6%
주 10
 
3.6%
서 8
 
2.9%
군 7
 
2.5%
진 7
 
2.5%
Other values (55) 115
41.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 277
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 51
18.4%
시 36
 
13.0%
전 12
 
4.3%
체 11
 
4.0%
성 10
 
3.6%
산 10
 
3.6%
주 10
 
3.6%
서 8
 
2.9%
군 7
 
2.5%
진 7
 
2.5%
Other values (55) 115
41.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 277
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 51
18.4%
시 36
 
13.0%
전 12
 
4.3%
체 11
 
4.0%
성 10
 
3.6%
산 10
 
3.6%
주 10
 
3.6%
서 8
 
2.9%
군 7
 
2.5%
진 7
 
2.5%
Other values (55) 115
41.5%

81
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.34343
Minimum8
Maximum343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:45:27.905691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile26.8
Q175
median102
Q3137
95-th percentile211.5
Maximum343
Range335
Interquartile range (IQR)62

Descriptive statistics

Standard deviation55.757888
Coefficient of variation (CV)0.50993357
Kurtosis2.674959
Mean109.34343
Median Absolute Deviation (MAD)30
Skewness1.1166899
Sum10825
Variance3108.9421
MonotonicityNot monotonic
2023-12-10T15:45:28.153322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 4
 
4.0%
73 3
 
3.0%
72 2
 
2.0%
79 2
 
2.0%
86 2
 
2.0%
192 2
 
2.0%
141 2
 
2.0%
115 2
 
2.0%
106 2
 
2.0%
105 2
 
2.0%
Other values (71) 76
76.8%
ValueCountFrequency (%)
8 1
1.0%
12 1
1.0%
15 1
1.0%
23 1
1.0%
25 1
1.0%
27 1
1.0%
34 1
1.0%
35 1
1.0%
43 1
1.0%
45 1
1.0%
ValueCountFrequency (%)
343 1
1.0%
248 1
1.0%
243 1
1.0%
238 1
1.0%
234 1
1.0%
209 1
1.0%
196 1
1.0%
192 2
2.0%
180 1
1.0%
174 1
1.0%

Interactions

2023-12-10T15:45:22.351588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:20.982621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.440669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.895376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:22.482783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.091428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.578892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.993201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:22.605921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.210628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.686916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:22.116131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:22.732122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.322730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:21.803051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:45:22.227218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:45:28.333833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201032202010.120인천광역시미추홀구81
20101.0000.0001.0000.0000.0000.8870.481
32200.0001.0000.0000.3270.0000.9540.532
2010.11.0000.0001.0000.0000.0000.8870.481
200.0000.3270.0001.0000.5460.0000.380
인천광역시0.0000.0000.0000.5461.0000.9690.214
미추홀구0.8870.9540.8870.0000.9691.0000.942
810.4810.5320.4810.3800.2140.9421.000
2023-12-10T15:45:28.461386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인천광역시20
인천광역시1.0000.323
200.3231.000
2023-12-10T15:45:28.585953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201032202010.18120인천광역시
20101.0000.5551.0000.5700.0000.000
32200.5551.0000.5550.6710.2130.000
2010.11.0000.5551.0000.5700.0000.000
810.5700.6710.5701.0000.1730.071
200.0000.2130.0000.1731.0000.323
인천광역시0.0000.0000.0000.0710.3231.000

Missing values

2023-12-10T15:45:22.923271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:45:23.135028image/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

MT201901201032202010.1DIY용품전체전체.1120인천광역시미추홀구81
0MT20190115356153DIY용품전체전체120인천광역시부평구97
1MT201901175156175DIY용품전체전체120인천광역시계양구86
2MT201901201119201DIY용품전체전체120대전광역시유성구125
3MT20190122376223DIY용품전체전체120울산광역시남구96
4MT201901394139DIY용품전체전체120경기도동두천시76
5MT201901338149338DIY용품전체전체120경기도안산시248
6MT20190143498434DIY용품전체전체120경기도의왕시93
7MT20190132870328DIY용품전체전체120경기도용인시108
8MT201901542254DIY용품전체전체120경기도안성시46
9MT201901549054DIY용품전체전체120강원도전체81
MT201901201032202010.1DIY용품전체전체.1120인천광역시미추홀구81
89MT201901685968DIY용품전체전체130대구광역시북구93
90MT201901969DIY용품전체전체130대구광역시수성구8
91MT20190112742127DIY용품전체전체130인천광역시중구110
92MT201901463746DIY용품전체전체130인천광역시남동구80
93MT201901645864DIY용품전체전체130인천광역시서구113
94MT201901705670DIY용품전체전체130광주광역시서구96
95MT201901190177190DIY용품전체전체130광주광역시북구135
96MT20190112282122DIY용품전체전체130광주광역시광산구115
97MT20190110263102DIY용품전체전체130울산광역시전체86
98MT20190111361113DIY용품전체전체130울산광역시북구83