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=ZTO010BSICGIRICECOO

Alerts

MT has constant value ""Constant
201803 has constant value ""Constant
전기밥솥 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
대유위니아 has constant value ""Constant
40 is highly overall correlated with 2High correlation
2 is highly overall correlated with 5 and 1 other fieldsHigh correlation
5 is highly overall correlated with 2High correlation
5 has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:24:05.090914
Analysis finished2023-12-10 06:24:06.872008
Duration1.78 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

2023-12-10T15:24:06.989655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:07.145928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201803
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201803
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201803
2nd row201803
3rd row201803
4th row201803
5th row201803

Common Values

ValueCountFrequency (%)
201803 99
100.0%

Length

2023-12-10T15:24:07.305537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:07.451303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201803 99
100.0%

44
Real number (ℝ)

Distinct83
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.23232
Minimum18
Maximum461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:24:07.625470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile28.4
Q174.5
median120
Q3194.5
95-th percentile296.2
Maximum461
Range443
Interquartile range (IQR)120

Descriptive statistics

Standard deviation85.715337
Coefficient of variation (CV)0.6246002
Kurtosis0.9279693
Mean137.23232
Median Absolute Deviation (MAD)66
Skewness0.85979961
Sum13586
Variance7347.1189
MonotonicityNot monotonic
2023-12-10T15:24:07.862968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5
 
5.1%
92 3
 
3.0%
46 2
 
2.0%
229 2
 
2.0%
30 2
 
2.0%
41 2
 
2.0%
84 2
 
2.0%
23 2
 
2.0%
199 2
 
2.0%
48 2
 
2.0%
Other values (73) 75
75.8%
ValueCountFrequency (%)
18 1
1.0%
19 1
1.0%
21 1
1.0%
23 2
2.0%
29 1
1.0%
30 2
2.0%
32 1
1.0%
36 1
1.0%
40 1
1.0%
41 2
2.0%
ValueCountFrequency (%)
461 1
1.0%
336 1
1.0%
311 1
1.0%
308 1
1.0%
307 1
1.0%
295 1
1.0%
268 1
1.0%
261 1
1.0%
256 1
1.0%
244 1
1.0%

5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.979798
Minimum0
Maximum100
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:24:08.549369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q317.5
95-th percentile88.3
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.415493
Coefficient of variation (CV)1.3790207
Kurtosis6.4918649
Mean16.979798
Median Absolute Deviation (MAD)6
Skewness2.6639735
Sum1681
Variance548.2853
MonotonicityNot monotonic
2023-12-10T15:24:08.799595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 7
 
7.1%
5 7
 
7.1%
7 7
 
7.1%
11 7
 
7.1%
8 6
 
6.1%
2 6
 
6.1%
9 6
 
6.1%
6 6
 
6.1%
1 5
 
5.1%
100 4
 
4.0%
Other values (24) 38
38.4%
ValueCountFrequency (%)
0 2
 
2.0%
1 5
5.1%
2 6
6.1%
3 7
7.1%
5 7
7.1%
6 6
6.1%
7 7
7.1%
8 6
6.1%
9 6
6.1%
10 3
3.0%
ValueCountFrequency (%)
100 4
4.0%
91 1
 
1.0%
88 1
 
1.0%
80 1
 
1.0%
57 1
 
1.0%
36 1
 
1.0%
35 1
 
1.0%
32 1
 
1.0%
29 2
2.0%
28 2
2.0%

전기밥솥
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전기밥솥 99
100.0%

Length

2023-12-10T15:24:09.049074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:09.215237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전기밥솥 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

2023-12-10T15:24:09.390176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:09.559189image/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

2023-12-10T15:24:09.740268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:09.991497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
87 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 87
87.9%
1 12
 
12.1%

Length

2023-12-10T15:24:10.180694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:10.340804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 87
87.9%
1 12
 
12.1%

40
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
40
38 
50
29 
60
16 
20
12 
70
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40

Common Values

ValueCountFrequency (%)
40 38
38.4%
50 29
29.3%
60 16
16.2%
20 12
 
12.1%
70 4
 
4.0%

Length

2023-12-10T15:24:10.549076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:10.737461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40 38
38.4%
50 29
29.3%
60 16
16.2%
20 12
 
12.1%
70 4
 
4.0%

부산광역시
Categorical

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
23 
서울특별시
15 
광주광역시
10 
경상북도
9 
대구광역시
6 
Other values (12)
36 

Length

Max length7
Median length5
Mean length4.2424242
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row부산광역시
2nd row광주광역시
3rd row대전광역시
4th row대전광역시
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 23
23.2%
서울특별시 15
15.2%
광주광역시 10
10.1%
경상북도 9
 
9.1%
대구광역시 6
 
6.1%
대전광역시 6
 
6.1%
충청북도 5
 
5.1%
충청남도 4
 
4.0%
강원도 4
 
4.0%
부산광역시 4
 
4.0%
Other values (7) 13
13.1%

Length

2023-12-10T15:24:11.019457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 23
23.2%
서울특별시 15
15.2%
광주광역시 10
10.1%
경상북도 9
 
9.1%
대구광역시 6
 
6.1%
대전광역시 6
 
6.1%
충청북도 5
 
5.1%
부산광역시 4
 
4.0%
강원도 4
 
4.0%
충청남도 4
 
4.0%
Other values (7) 13
13.1%

북구
Text

Distinct50
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:24:11.335577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6969697
Min length2

Characters and Unicode

Total characters267
Distinct characters61
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

Unique35 ?
Unique (%)35.4%

Sample

1st row수영구
2nd row전체
3rd row전체
4th row동구
5th row수원시
ValueCountFrequency (%)
전체 22
22.2%
북구 5
 
5.1%
수원시 4
 
4.0%
성남시 4
 
4.0%
시흥시 4
 
4.0%
고양시 4
 
4.0%
동구 3
 
3.0%
강북구 3
 
3.0%
남구 3
 
3.0%
금천구 2
 
2.0%
Other values (40) 45
45.5%
2023-12-10T15:24:11.902559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 45
16.9%
구 33
 
12.4%
전 23
 
8.6%
체 22
 
8.2%
천 9
 
3.4%
북 8
 
3.0%
남 8
 
3.0%
동 8
 
3.0%
수 7
 
2.6%
성 7
 
2.6%
Other values (51) 97
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 267
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 45
16.9%
구 33
 
12.4%
전 23
 
8.6%
체 22
 
8.2%
천 9
 
3.4%
북 8
 
3.0%
남 8
 
3.0%
동 8
 
3.0%
수 7
 
2.6%
성 7
 
2.6%
Other values (51) 97
36.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 267
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 45
16.9%
구 33
 
12.4%
전 23
 
8.6%
체 22
 
8.2%
천 9
 
3.4%
북 8
 
3.0%
남 8
 
3.0%
동 8
 
3.0%
수 7
 
2.6%
성 7
 
2.6%
Other values (51) 97
36.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 267
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 45
16.9%
구 33
 
12.4%
전 23
 
8.6%
체 22
 
8.2%
천 9
 
3.4%
북 8
 
3.0%
남 8
 
3.0%
동 8
 
3.0%
수 7
 
2.6%
성 7
 
2.6%
Other values (51) 97
36.3%

대유위니아
Categorical

CONSTANT 

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

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대유위니아 99
100.0%

Length

2023-12-10T15:24:12.167189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:24:12.320977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대유위니아 99
100.0%

Interactions

2023-12-10T15:24:06.052791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.688150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:06.199307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:05.897596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:24:12.428775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
445240부산광역시북구
441.0000.2780.3010.0000.5360.000
50.2781.0000.8020.4870.1250.836
20.3010.8021.0001.0000.0000.000
400.0000.4871.0001.0000.0000.000
부산광역시0.5360.1250.0000.0001.0000.938
북구0.0000.8360.0000.0000.9381.000
2023-12-10T15:24:12.633204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
40부산광역시2
401.0000.0000.984
부산광역시0.0001.0000.000
20.9840.0001.000
2023-12-10T15:24:12.852141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
445240부산광역시
441.0000.4540.2880.0000.232
50.4541.0000.6050.3220.000
20.2880.6051.0000.9840.000
400.0000.3220.9841.0000.000
부산광역시0.2320.0000.0000.0001.000

Missing values

2023-12-10T15:24:06.450882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:24:06.763000image/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

MT201803445전기밥솥전체전체.1240부산광역시북구대유위니아
0MT201803463전기밥솥전체전체240부산광역시수영구대유위니아
1MT20180322911전기밥솥전체전체240광주광역시전체대유위니아
2MT2018031855전기밥솥전체전체240대전광역시전체대유위니아
3MT2018031397전기밥솥전체전체240대전광역시동구대유위니아
4MT2018031303전기밥솥전체전체240경기도수원시대유위니아
5MT2018031113전기밥솥전체전체240경기도성남시대유위니아
6MT20180318917전기밥솥전체전체240경기도이천시대유위니아
7MT20180324311전기밥솥전체전체240충청북도전체대유위니아
8MT201803320전기밥솥전체전체240경상북도전체대유위니아
9MT2018039829전기밥솥전체전체240경상남도통영시대유위니아
MT201803445전기밥솥전체전체.1240부산광역시북구대유위니아
89MT2018032448전기밥솥전체전체240제주특별자치도전체대유위니아
90MT2018032618전기밥솥전체전체240제주특별자치도제주시대유위니아
91MT2018032117전기밥솥전체전체250전체전체대유위니아
92MT2018031696전기밥솥전체전체250서울특별시도봉구대유위니아
93MT20180314119전기밥솥전체전체250서울특별시동작구대유위니아
94MT20180319011전기밥솥전체전체250서울특별시서초구대유위니아
95MT201803232전기밥솥전체전체250부산광역시부산진구대유위니아
96MT201803526전기밥솥전체전체250부산광역시기장군대유위니아
97MT20180329521전기밥솥전체전체250대구광역시북구대유위니아
98MT201803985전기밥솥전체전체250대구광역시수성구대유위니아