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=ZTO011TOTALCGIINFAN

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
분유류 has constant value ""Constant
조제분유 has constant value ""Constant
조제분유.1 has constant value ""Constant
1 has constant value ""Constant
92 is highly overall correlated with 92.1High correlation
81 is highly overall correlated with 93High correlation
92.1 is highly overall correlated with 92High correlation
93 is highly overall correlated with 81High correlation

Reproduction

Analysis started2023-12-10 06:37:36.775130
Analysis finished2023-12-10 06:37:40.370245
Duration3.6 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

2023-12-10T15:37:40.462489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:40.598365image/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

2023-12-10T15:37:40.759863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:40.897242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

92
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92929
Minimum-19
Maximum647
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:37:41.062338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile24.4
Q174.5
median93
Q3136.5
95-th percentile424.4
Maximum647
Range666
Interquartile range (IQR)62

Descriptive statistics

Standard deviation118.37326
Coefficient of variation (CV)0.93259216
Kurtosis8.837953
Mean126.92929
Median Absolute Deviation (MAD)25
Skewness2.8614634
Sum12566
Variance14012.23
MonotonicityNot monotonic
2023-12-10T15:37:41.303317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 4
 
4.0%
89 3
 
3.0%
113 2
 
2.0%
76 2
 
2.0%
90 2
 
2.0%
71 2
 
2.0%
88 2
 
2.0%
103 2
 
2.0%
78 2
 
2.0%
146 2
 
2.0%
Other values (69) 76
76.8%
ValueCountFrequency (%)
-19 1
1.0%
1 1
1.0%
12 1
1.0%
18 1
1.0%
19 1
1.0%
25 1
1.0%
29 1
1.0%
34 1
1.0%
35 1
1.0%
39 1
1.0%
ValueCountFrequency (%)
647 1
1.0%
594 1
1.0%
591 1
1.0%
543 1
1.0%
428 1
1.0%
424 1
1.0%
286 1
1.0%
222 1
1.0%
211 1
1.0%
206 1
1.0%

81
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.26263
Minimum-22
Maximum1035
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:37:41.558798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile27.2
Q168
median87
Q3112.5
95-th percentile185.1
Maximum1035
Range1057
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation107.84386
Coefficient of variation (CV)1.0443649
Kurtosis57.864136
Mean103.26263
Median Absolute Deviation (MAD)20
Skewness6.8690835
Sum10223
Variance11630.298
MonotonicityNot monotonic
2023-12-10T15:37:41.854830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 4
 
4.0%
70 3
 
3.0%
69 3
 
3.0%
80 3
 
3.0%
90 2
 
2.0%
56 2
 
2.0%
157 2
 
2.0%
55 2
 
2.0%
97 2
 
2.0%
105 2
 
2.0%
Other values (67) 74
74.7%
ValueCountFrequency (%)
-22 1
1.0%
7 1
1.0%
12 1
1.0%
15 1
1.0%
20 1
1.0%
28 1
1.0%
30 1
1.0%
37 1
1.0%
43 1
1.0%
46 1
1.0%
ValueCountFrequency (%)
1035 1
1.0%
359 1
1.0%
287 1
1.0%
206 1
1.0%
195 1
1.0%
184 1
1.0%
172 1
1.0%
162 1
1.0%
157 2
2.0%
152 1
1.0%

92.1
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92929
Minimum-19
Maximum647
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:37:42.169418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile24.4
Q174.5
median93
Q3136.5
95-th percentile424.4
Maximum647
Range666
Interquartile range (IQR)62

Descriptive statistics

Standard deviation118.37326
Coefficient of variation (CV)0.93259216
Kurtosis8.837953
Mean126.92929
Median Absolute Deviation (MAD)25
Skewness2.8614634
Sum12566
Variance14012.23
MonotonicityNot monotonic
2023-12-10T15:37:42.554449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 4
 
4.0%
89 3
 
3.0%
113 2
 
2.0%
76 2
 
2.0%
90 2
 
2.0%
71 2
 
2.0%
88 2
 
2.0%
103 2
 
2.0%
78 2
 
2.0%
146 2
 
2.0%
Other values (69) 76
76.8%
ValueCountFrequency (%)
-19 1
1.0%
1 1
1.0%
12 1
1.0%
18 1
1.0%
19 1
1.0%
25 1
1.0%
29 1
1.0%
34 1
1.0%
35 1
1.0%
39 1
1.0%
ValueCountFrequency (%)
647 1
1.0%
594 1
1.0%
591 1
1.0%
543 1
1.0%
428 1
1.0%
424 1
1.0%
286 1
1.0%
222 1
1.0%
211 1
1.0%
206 1
1.0%

분유류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분유류 99
100.0%

Length

2023-12-10T15:37:42.795448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:42.960093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분유류 99
100.0%

조제분유
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
조제분유 99
100.0%

Length

2023-12-10T15:37:43.109125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:43.325569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조제분유 99
100.0%

조제분유.1
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
조제분유 99
100.0%

Length

2023-12-10T15:37:43.538001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:43.726014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조제분유 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

2023-12-10T15:37:43.952819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:44.112480image/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
62 
20
25 
40
12 

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 62
62.6%
20 25
25.3%
40 12
 
12.1%

Length

2023-12-10T15:37:44.300982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:37:44.447781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 62
62.6%
20 25
25.3%
40 12
 
12.1%

경기도
Categorical

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
17 
충청남도
12 
경상남도
7 
부산광역시
7 
서울특별시
7 
Other values (12)
49 

Length

Max length7
Median length5
Mean length4.2020202
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경기도
2nd row강원도
3rd row강원도
4th row충청남도
5th row전라북도

Common Values

ValueCountFrequency (%)
경기도 17
17.2%
충청남도 12
12.1%
경상남도 7
 
7.1%
부산광역시 7
 
7.1%
서울특별시 7
 
7.1%
전라남도 6
 
6.1%
강원도 6
 
6.1%
인천광역시 6
 
6.1%
울산광역시 5
 
5.1%
광주광역시 5
 
5.1%
Other values (7) 21
21.2%

Length

2023-12-10T15:37:44.618337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 17
17.2%
충청남도 12
12.1%
경상남도 7
 
7.1%
부산광역시 7
 
7.1%
서울특별시 7
 
7.1%
전라남도 6
 
6.1%
강원도 6
 
6.1%
인천광역시 6
 
6.1%
광주광역시 5
 
5.1%
울산광역시 5
 
5.1%
Other values (7) 21
21.2%

구리시
Text

Distinct53
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:37:44.919475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7777778
Min length2

Characters and Unicode

Total characters275
Distinct characters60
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

Unique24 ?
Unique (%)24.2%

Sample

1st row김포시
2nd row춘천시
3rd row원주시
4th row천안시
5th row전주시
ValueCountFrequency (%)
전체 14
 
14.1%
북구 5
 
5.1%
서구 4
 
4.0%
진주시 2
 
2.0%
구미시 2
 
2.0%
무안군 2
 
2.0%
당진시 2
 
2.0%
서산시 2
 
2.0%
김포시 2
 
2.0%
충주시 2
 
2.0%
Other values (43) 62
62.6%
2023-12-10T15:37:45.499940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 50
18.2%
구 32
 
11.6%
전 16
 
5.8%
주 15
 
5.5%
체 14
 
5.1%
산 12
 
4.4%
서 9
 
3.3%
군 9
 
3.3%
안 8
 
2.9%
성 8
 
2.9%
Other values (50) 102
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 275
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 50
18.2%
구 32
 
11.6%
전 16
 
5.8%
주 15
 
5.5%
체 14
 
5.1%
산 12
 
4.4%
서 9
 
3.3%
군 9
 
3.3%
안 8
 
2.9%
성 8
 
2.9%
Other values (50) 102
37.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 275
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 50
18.2%
구 32
 
11.6%
전 16
 
5.8%
주 15
 
5.5%
체 14
 
5.1%
산 12
 
4.4%
서 9
 
3.3%
군 9
 
3.3%
안 8
 
2.9%
성 8
 
2.9%
Other values (50) 102
37.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 275
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 50
18.2%
구 32
 
11.6%
전 16
 
5.8%
주 15
 
5.5%
체 14
 
5.1%
산 12
 
4.4%
서 9
 
3.3%
군 9
 
3.3%
안 8
 
2.9%
성 8
 
2.9%
Other values (50) 102
37.1%

93
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.59596
Minimum-72
Maximum172
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:37:45.732448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-72
5-th percentile38.9
Q166
median88
Q3103
95-th percentile146.5
Maximum172
Range244
Interquartile range (IQR)37

Descriptive statistics

Standard deviation34.853313
Coefficient of variation (CV)0.40248198
Kurtosis3.9970648
Mean86.59596
Median Absolute Deviation (MAD)18
Skewness-0.68128484
Sum8573
Variance1214.7535
MonotonicityNot monotonic
2023-12-10T15:37:45.959184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 7
 
7.1%
86 3
 
3.0%
48 3
 
3.0%
78 3
 
3.0%
106 3
 
3.0%
90 3
 
3.0%
64 3
 
3.0%
87 3
 
3.0%
103 2
 
2.0%
101 2
 
2.0%
Other values (58) 67
67.7%
ValueCountFrequency (%)
-72 1
 
1.0%
3 1
 
1.0%
17 1
 
1.0%
36 1
 
1.0%
38 1
 
1.0%
39 1
 
1.0%
43 1
 
1.0%
46 1
 
1.0%
47 1
 
1.0%
48 3
3.0%
ValueCountFrequency (%)
172 1
1.0%
169 1
1.0%
162 1
1.0%
159 1
1.0%
151 1
1.0%
146 1
1.0%
139 1
1.0%
135 1
1.0%
134 1
1.0%
128 1
1.0%

Interactions

2023-12-10T15:37:39.271013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:37.312947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:37.830969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:38.607229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:39.397078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:37.439045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:37.963934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:38.733733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:39.537172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:37.552596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:38.105910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:38.866031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:39.757098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:37.715402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:38.241101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:37:39.062607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:37:46.140341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
928192.120경기도구리시93
921.0000.6951.0000.3970.0000.7880.703
810.6951.0000.6950.0000.2590.5270.512
92.11.0000.6951.0000.3970.0000.7880.703
200.3970.0000.3971.0000.3900.0000.297
경기도0.0000.2590.0000.3901.0000.9720.000
구리시0.7880.5270.7880.0000.9721.0000.870
930.7030.5120.7030.2970.0000.8701.000
2023-12-10T15:37:46.374520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20경기도
201.0000.208
경기도0.2081.000
2023-12-10T15:37:46.529139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
928192.19320경기도
921.0000.3081.0000.4380.2670.000
810.3081.0000.3080.5240.0000.120
92.11.0000.3081.0000.4380.2670.000
930.4380.5240.4381.0000.1800.000
200.2670.0000.2670.1801.0000.208
경기도0.0000.1200.0000.0000.2081.000

Missing values

2023-12-10T15:37:39.970604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:37:40.272965image/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

MT201901928192.1분유류조제분유조제분유.1120경기도구리시93
0MT20190118270182분유류조제분유조제분유120경기도김포시86
1MT201901769076분유류조제분유조제분유120강원도춘천시78
2MT20190142488424분유류조제분유조제분유120강원도원주시103
3MT20190115681156분유류조제분유조제분유120충청남도천안시52
4MT201901543359543분유류조제분유조제분유120전라북도전주시134
5MT201901897589분유류조제분유조제분유120전라북도군산시39
6MT201901647113647분유류조제분유조제분유120전라남도나주시169
7MT201901756875분유류조제분유조제분유120경상남도전체47
8MT20190113646136분유류조제분유조제분유120경상남도창원시48
9MT2019016914769분유류조제분유조제분유120제주특별자치도전체64
MT201901928192.1분유류조제분유조제분유.1120경기도구리시93
89MT201901536753분유류조제분유조제분유130충청남도아산시80
90MT20190111688116분유류조제분유조제분유130충청남도서산시99
91MT201901679767분유류조제분유조제분유130충청남도당진시78
92MT201901748074분유류조제분유조제분유130충청남도홍성군65
93MT20190111091110분유류조제분유조제분유130전라남도여수시94
94MT201901506750분유류조제분유조제분유130전라남도무안군88
95MT201901967896분유류조제분유조제분유130경상북도구미시106
96MT201901146116146분유류조제분유조제분유130경상남도진주시102
97MT20190110330103분유류조제분유조제분유130경상남도합천군50
98MT201901788478분유류조제분유조제분유140서울특별시전체104