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

Categorical9
Numeric4

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO011TOTALCGIGARDE

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
원예용품 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
2 has constant value ""Constant
133 is highly overall correlated with 133.1High correlation
133.1 is highly overall correlated with 133High correlation
서울특별시 is highly overall correlated with 전체.2High correlation
전체.2 is highly overall correlated with 서울특별시High correlation

Reproduction

Analysis started2023-12-10 06:44:08.298913
Analysis finished2023-12-10 06:44:11.820401
Duration3.52 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

2023-12-10T15:44:11.913448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:12.050135image/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

2023-12-10T15:44:12.206471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:12.361491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

133
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean768.90909
Minimum-1890
Maximum24483
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:44:12.525099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1890
5-th percentile64.8
Q1105
median161
Q3343.5
95-th percentile2029.9
Maximum24483
Range26373
Interquartile range (IQR)238.5

Descriptive statistics

Standard deviation2919.9745
Coefficient of variation (CV)3.7975549
Kurtosis50.147376
Mean768.90909
Median Absolute Deviation (MAD)70
Skewness6.8003741
Sum76122
Variance8526251.2
MonotonicityNot monotonic
2023-12-10T15:44:12.778706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 3
 
3.0%
85 2
 
2.0%
161 2
 
2.0%
1063 2
 
2.0%
117 2
 
2.0%
348 2
 
2.0%
139 2
 
2.0%
92 2
 
2.0%
101 2
 
2.0%
306 1
 
1.0%
Other values (79) 79
79.8%
ValueCountFrequency (%)
-1890 1
1.0%
27 1
1.0%
29 1
1.0%
36 1
1.0%
54 1
1.0%
66 1
1.0%
67 1
1.0%
75 1
1.0%
79 1
1.0%
85 2
2.0%
ValueCountFrequency (%)
24483 1
1.0%
14899 1
1.0%
5654 1
1.0%
2866 1
1.0%
2686 1
1.0%
1957 1
1.0%
1873 1
1.0%
1720 1
1.0%
1501 1
1.0%
1293 1
1.0%

97
Real number (ℝ)

Distinct78
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.22222
Minimum22
Maximum1089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:44:13.006617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile46.7
Q175
median97
Q3124.5
95-th percentile370.2
Maximum1089
Range1067
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation167.16169
Coefficient of variation (CV)1.2181824
Kurtosis21.38478
Mean137.22222
Median Absolute Deviation (MAD)25
Skewness4.437437
Sum13585
Variance27943.032
MonotonicityNot monotonic
2023-12-10T15:44:13.275181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 4
 
4.0%
84 2
 
2.0%
85 2
 
2.0%
75 2
 
2.0%
70 2
 
2.0%
114 2
 
2.0%
97 2
 
2.0%
107 2
 
2.0%
76 2
 
2.0%
110 2
 
2.0%
Other values (68) 77
77.8%
ValueCountFrequency (%)
22 1
1.0%
34 1
1.0%
35 1
1.0%
42 1
1.0%
44 1
1.0%
47 1
1.0%
50 1
1.0%
51 1
1.0%
53 1
1.0%
55 1
1.0%
ValueCountFrequency (%)
1089 1
1.0%
1074 1
1.0%
743 1
1.0%
542 1
1.0%
399 1
1.0%
367 1
1.0%
280 1
1.0%
261 1
1.0%
209 1
1.0%
199 1
1.0%

133.1
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean768.90909
Minimum-1890
Maximum24483
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:44:13.486182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1890
5-th percentile64.8
Q1105
median161
Q3343.5
95-th percentile2029.9
Maximum24483
Range26373
Interquartile range (IQR)238.5

Descriptive statistics

Standard deviation2919.9745
Coefficient of variation (CV)3.7975549
Kurtosis50.147376
Mean768.90909
Median Absolute Deviation (MAD)70
Skewness6.8003741
Sum76122
Variance8526251.2
MonotonicityNot monotonic
2023-12-10T15:44:13.731206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 3
 
3.0%
85 2
 
2.0%
161 2
 
2.0%
1063 2
 
2.0%
117 2
 
2.0%
348 2
 
2.0%
139 2
 
2.0%
92 2
 
2.0%
101 2
 
2.0%
306 1
 
1.0%
Other values (79) 79
79.8%
ValueCountFrequency (%)
-1890 1
1.0%
27 1
1.0%
29 1
1.0%
36 1
1.0%
54 1
1.0%
66 1
1.0%
67 1
1.0%
75 1
1.0%
79 1
1.0%
85 2
2.0%
ValueCountFrequency (%)
24483 1
1.0%
14899 1
1.0%
5654 1
1.0%
2866 1
1.0%
2686 1
1.0%
1957 1
1.0%
1873 1
1.0%
1720 1
1.0%
1501 1
1.0%
1293 1
1.0%

원예용품
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원예용품 99
100.0%

Length

2023-12-10T15:44:13.972000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:14.107060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원예용품 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

2023-12-10T15:44:14.277116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:14.438236image/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

2023-12-10T15:44:14.572554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:14.709372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

2
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
99 

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 99
100.0%

Length

2023-12-10T15:44:14.864015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:15.016282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 99
100.0%

20
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
30
45 
20
41 
40
13 

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 45
45.5%
20 41
41.4%
40 13
 
13.1%

Length

2023-12-10T15:44:15.174615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:44:15.330626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 45
45.5%
20 41
41.4%
40 13
 
13.1%

서울특별시
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
17 
충청남도
12 
부산광역시
10 
서울특별시
9 
인천광역시
9 
Other values (12)
42 

Length

Max length7
Median length5
Mean length4.2323232
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 17
17.2%
충청남도 12
12.1%
부산광역시 10
10.1%
서울특별시 9
9.1%
인천광역시 9
9.1%
울산광역시 7
7.1%
강원도 6
 
6.1%
광주광역시 4
 
4.0%
전라북도 4
 
4.0%
경상남도 4
 
4.0%
Other values (7) 17
17.2%

Length

2023-12-10T15:44:15.507770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 17
17.2%
충청남도 12
12.1%
부산광역시 10
10.1%
서울특별시 9
9.1%
인천광역시 9
9.1%
울산광역시 7
7.1%
강원도 6
 
6.1%
경상남도 4
 
4.0%
전라남도 4
 
4.0%
전라북도 4
 
4.0%
Other values (7) 17
17.2%

전체.2
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
12 
서구
 
4
북구
 
4
남구
 
3
계양구
 
3
Other values (39)
73 

Length

Max length4
Median length3
Mean length2.8080808
Min length2

Unique

Unique11 ?
Unique (%)11.1%

Sample

1st row구로구
2nd row사하구
3rd row부평구
4th row계양구
5th row유성구

Common Values

ValueCountFrequency (%)
전체 12
 
12.1%
서구 4
 
4.0%
북구 4
 
4.0%
남구 3
 
3.0%
계양구 3
 
3.0%
유성구 3
 
3.0%
부평구 3
 
3.0%
구로구 3
 
3.0%
안산시 3
 
3.0%
사하구 3
 
3.0%
Other values (34) 58
58.6%

Length

2023-12-10T15:44:15.702548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 12
 
12.1%
북구 4
 
4.0%
서구 4
 
4.0%
구로구 3
 
3.0%
사하구 3
 
3.0%
안산시 3
 
3.0%
동두천시 3
 
3.0%
부평구 3
 
3.0%
유성구 3
 
3.0%
계양구 3
 
3.0%
Other values (34) 58
58.6%

66
Real number (ℝ)

Distinct62
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.111111
Minimum3
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:44:15.937324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.3
Q154
median73
Q386.5
95-th percentile127.5
Maximum199
Range196
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation30.884871
Coefficient of variation (CV)0.42243745
Kurtosis2.793883
Mean73.111111
Median Absolute Deviation (MAD)17
Skewness0.82772659
Sum7238
Variance953.87528
MonotonicityNot monotonic
2023-12-10T15:44:16.191250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 6
 
6.1%
86 5
 
5.1%
76 4
 
4.0%
77 3
 
3.0%
50 3
 
3.0%
57 3
 
3.0%
60 3
 
3.0%
67 3
 
3.0%
81 2
 
2.0%
85 2
 
2.0%
Other values (52) 65
65.7%
ValueCountFrequency (%)
3 1
1.0%
9 1
1.0%
11 1
1.0%
12 1
1.0%
14 1
1.0%
31 1
1.0%
34 2
2.0%
39 1
1.0%
40 1
1.0%
43 2
2.0%
ValueCountFrequency (%)
199 1
1.0%
165 1
1.0%
143 1
1.0%
134 1
1.0%
132 1
1.0%
127 1
1.0%
116 1
1.0%
112 1
1.0%
110 2
2.0%
108 1
1.0%

Interactions

2023-12-10T15:44:10.783028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:08.866932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.711346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.237086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.916493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.292740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.844452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.363643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:11.060353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.417228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.976009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.503155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:11.201679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:09.559490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.097208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:44:10.632591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:44:16.383839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
13397133.120서울특별시전체.266
1331.0000.0001.0000.1430.0000.8550.000
970.0001.0000.0000.1410.0000.8780.641
133.11.0000.0001.0000.1430.0000.8550.000
200.1430.1410.1431.0000.0000.0000.479
서울특별시0.0000.0000.0000.0001.0000.9810.225
전체.20.8550.8780.8550.0000.9811.0000.639
660.0000.6410.0000.4790.2250.6391.000
2023-12-10T15:44:16.572415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울특별시20전체.2
서울특별시1.0000.0000.648
200.0001.0000.000
전체.20.6480.0001.000
2023-12-10T15:44:16.721114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
13397133.16620서울특별시전체.2
1331.0000.2071.000-0.0280.1080.0000.452
970.2071.0000.2070.4950.0900.0000.460
133.11.0000.2071.000-0.0280.1080.0000.452
66-0.0280.495-0.0281.0000.3200.0650.202
200.1080.0900.1080.3201.0000.0000.000
서울특별시0.0000.0000.0000.0650.0001.0000.648
전체.20.4520.4600.4520.2020.0000.6481.000

Missing values

2023-12-10T15:44:11.423632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:44:11.712980image/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

MT20190113397133.1원예용품전체전체.1220서울특별시전체.266
0MT201901125155125원예용품전체전체220서울특별시구로구94
1MT20190132635326원예용품전체전체220부산광역시사하구11
2MT201901105115105원예용품전체전체220인천광역시부평구92
3MT2019018710487원예용품전체전체220인천광역시계양구76
4MT201901105156105원예용품전체전체220대전광역시유성구63
5MT201901607117607원예용품전체전체220울산광역시남구86
6MT201901421157421원예용품전체전체220경기도동두천시107
7MT20190126747267원예용품전체전체220경기도안산시46
8MT20190126861362686원예용품전체전체220경기도용인시77
9MT201901644367644원예용품전체전체220경기도안성시84
MT20190113397133.1원예용품전체전체.1220서울특별시전체.266
89MT20190116760167원예용품전체전체230울산광역시북구54
90MT201901128127128원예용품전체전체230경기도구리시57
91MT201901917591원예용품전체전체230경기도김포시108
92MT201901466542466원예용품전체전체230경기도화성시59
93MT20190129108929원예용품전체전체230경기도양주시12
94MT201901947694원예용품전체전체230강원도춘천시70
95MT20190117342173원예용품전체전체230강원도원주시46
96MT201901117110117원예용품전체전체230충청남도천안시110
97MT20190114469144원예용품전체전체230전라북도전주시43
98MT20190115297152원예용품전체전체230전라북도군산시73