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=ZTO011TOTALCGIDETER

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
세탁세제/섬유유연제 has constant value ""Constant
1 has constant value ""Constant
세탁세제 is highly overall correlated with 전체High correlation
전체 is highly overall correlated with 세탁세제High correlation
104 is highly overall correlated with 104.1High correlation
156 is highly overall correlated with 44High correlation
104.1 is highly overall correlated with 104High correlation
44 is highly overall correlated with 156High correlation
경상남도 is highly overall correlated with 전체.1High correlation
전체.1 is highly overall correlated with 경상남도High correlation

Reproduction

Analysis started2023-12-10 06:55:43.968642
Analysis finished2023-12-10 06:55:46.142474
Duration2.17 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

2023-12-10T15:55:46.207572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:46.315669image/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

2023-12-10T15:55:46.462476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:46.564025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

104
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.232323
Minimum-225
Maximum532
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.0%
Memory size1023.0 B
2023-12-10T15:55:46.688891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-225
5-th percentile30.8
Q173
median98
Q3114.5
95-th percentile171.8
Maximum532
Range757
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation67.102089
Coefficient of variation (CV)0.69012122
Kurtosis22.393803
Mean97.232323
Median Absolute Deviation (MAD)19
Skewness1.7765743
Sum9626
Variance4502.6904
MonotonicityNot monotonic
2023-12-10T15:55:46.836795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 4
 
4.0%
143 4
 
4.0%
56 3
 
3.0%
99 3
 
3.0%
86 3
 
3.0%
114 3
 
3.0%
120 3
 
3.0%
110 3
 
3.0%
89 2
 
2.0%
115 2
 
2.0%
Other values (56) 69
69.7%
ValueCountFrequency (%)
-225 1
1.0%
-1 1
1.0%
12 1
1.0%
19 1
1.0%
20 1
1.0%
32 1
1.0%
39 1
1.0%
46 1
1.0%
48 2
2.0%
49 2
2.0%
ValueCountFrequency (%)
532 1
 
1.0%
251 1
 
1.0%
190 1
 
1.0%
182 1
 
1.0%
179 1
 
1.0%
171 1
 
1.0%
166 1
 
1.0%
158 1
 
1.0%
143 4
4.0%
130 1
 
1.0%

156
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.585859
Minimum-2
Maximum439
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:55:47.014387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile27.7
Q173.5
median88
Q3100
95-th percentile214.3
Maximum439
Range441
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation61.967241
Coefficient of variation (CV)0.63500226
Kurtosis11.426915
Mean97.585859
Median Absolute Deviation (MAD)12
Skewness2.7961007
Sum9661
Variance3839.939
MonotonicityNot monotonic
2023-12-10T15:55:47.159282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 5
 
5.1%
100 4
 
4.0%
80 4
 
4.0%
92 4
 
4.0%
88 4
 
4.0%
94 4
 
4.0%
97 3
 
3.0%
83 3
 
3.0%
82 3
 
3.0%
91 2
 
2.0%
Other values (56) 63
63.6%
ValueCountFrequency (%)
-2 1
1.0%
8 1
1.0%
14 1
1.0%
15 1
1.0%
25 1
1.0%
28 1
1.0%
32 1
1.0%
33 1
1.0%
38 1
1.0%
43 1
1.0%
ValueCountFrequency (%)
439 1
1.0%
346 1
1.0%
264 1
1.0%
246 1
1.0%
235 1
1.0%
212 1
1.0%
188 1
1.0%
178 1
1.0%
168 1
1.0%
156 1
1.0%

104.1
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.232323
Minimum-225
Maximum532
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.0%
Memory size1023.0 B
2023-12-10T15:55:47.293860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-225
5-th percentile30.8
Q173
median98
Q3114.5
95-th percentile171.8
Maximum532
Range757
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation67.102089
Coefficient of variation (CV)0.69012122
Kurtosis22.393803
Mean97.232323
Median Absolute Deviation (MAD)19
Skewness1.7765743
Sum9626
Variance4502.6904
MonotonicityNot monotonic
2023-12-10T15:55:47.440989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 4
 
4.0%
143 4
 
4.0%
56 3
 
3.0%
99 3
 
3.0%
86 3
 
3.0%
114 3
 
3.0%
120 3
 
3.0%
110 3
 
3.0%
89 2
 
2.0%
115 2
 
2.0%
Other values (56) 69
69.7%
ValueCountFrequency (%)
-225 1
1.0%
-1 1
1.0%
12 1
1.0%
19 1
1.0%
20 1
1.0%
32 1
1.0%
39 1
1.0%
46 1
1.0%
48 2
2.0%
49 2
2.0%
ValueCountFrequency (%)
532 1
 
1.0%
251 1
 
1.0%
190 1
 
1.0%
182 1
 
1.0%
179 1
 
1.0%
171 1
 
1.0%
166 1
 
1.0%
158 1
 
1.0%
143 4
4.0%
130 1
 
1.0%

세탁세제/섬유유연제
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
세탁세제/섬유유연제
99 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세탁세제/섬유유연제
2nd row세탁세제/섬유유연제
3rd row세탁세제/섬유유연제
4th row세탁세제/섬유유연제
5th row세탁세제/섬유유연제

Common Values

ValueCountFrequency (%)
세탁세제/섬유유연제 99
100.0%

Length

2023-12-10T15:55:47.565655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:47.641806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁세제/섬유유연제 99
100.0%

세탁세제
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
세탁세제
56 
전체
43 

Length

Max length4
Median length4
Mean length3.1313131
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁세제 56
56.6%
전체 43
43.4%

Length

2023-12-10T15:55:47.744955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:47.848394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁세제 56
56.6%
전체 43
43.4%

전체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
세탁세제
56 
전체
43 

Length

Max length4
Median length4
Mean length3.1313131
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세탁세제 56
56.6%
전체 43
43.4%

Length

2023-12-10T15:55:47.957865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:48.056934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세탁세제 56
56.6%
전체 43
43.4%

1
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

2023-12-10T15:55:48.149238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:48.237939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 99
100.0%

99
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
20
85 
30
14 

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 (%)
20 85
85.9%
30 14
 
14.1%

Length

2023-12-10T15:55:48.333579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:55:48.425063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 85
85.9%
30 14
 
14.1%

경상남도
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
16 
서울특별시
12 
대구광역시
9 
인천광역시
9 
강원도
8 
Other values (11)
45 

Length

Max length7
Median length5
Mean length4.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 16
16.2%
서울특별시 12
12.1%
대구광역시 9
9.1%
인천광역시 9
9.1%
강원도 8
8.1%
광주광역시 7
7.1%
부산광역시 6
 
6.1%
경상북도 6
 
6.1%
울산광역시 5
 
5.1%
전라북도 4
 
4.0%
Other values (6) 17
17.2%

Length

2023-12-10T15:55:48.532060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 16
16.2%
서울특별시 12
12.1%
대구광역시 9
9.1%
인천광역시 9
9.1%
강원도 8
8.1%
광주광역시 7
7.1%
부산광역시 6
 
6.1%
경상북도 6
 
6.1%
울산광역시 5
 
5.1%
전라북도 4
 
4.0%
Other values (6) 17
17.2%

전체.1
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
11 
북구
7 
중구
 
6
서구
 
6
수성구
 
3
Other values (31)
66 

Length

Max length4
Median length3
Mean length2.7272727
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row전체
2nd row도봉구
3rd row양천구
4th row서초구
5th row강남구

Common Values

ValueCountFrequency (%)
전체 11
 
11.1%
북구 7
 
7.1%
중구 6
 
6.1%
서구 6
 
6.1%
수성구 3
 
3.0%
부산진구 3
 
3.0%
남동구 3
 
3.0%
도봉구 3
 
3.0%
양천구 3
 
3.0%
서초구 3
 
3.0%
Other values (26) 51
51.5%

Length

2023-12-10T15:55:48.669540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 11
 
11.1%
북구 7
 
7.1%
중구 6
 
6.1%
서구 6
 
6.1%
양천구 3
 
3.0%
기장군 3
 
3.0%
서초구 3
 
3.0%
강남구 3
 
3.0%
도봉구 3
 
3.0%
남동구 3
 
3.0%
Other values (26) 51
51.5%

44
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.222222
Minimum-1
Maximum226
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:55:48.807589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile29.7
Q168
median84
Q392.5
95-th percentile157.4
Maximum226
Range227
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation37.641585
Coefficient of variation (CV)0.44693174
Kurtosis3.9275464
Mean84.222222
Median Absolute Deviation (MAD)14
Skewness1.2949019
Sum8338
Variance1416.8889
MonotonicityNot monotonic
2023-12-10T15:55:48.946847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 7
 
7.1%
89 5
 
5.1%
87 5
 
5.1%
84 4
 
4.0%
81 3
 
3.0%
70 3
 
3.0%
74 3
 
3.0%
94 2
 
2.0%
80 2
 
2.0%
78 2
 
2.0%
Other values (53) 63
63.6%
ValueCountFrequency (%)
-1 1
1.0%
15 1
1.0%
17 1
1.0%
20 1
1.0%
27 1
1.0%
30 1
1.0%
31 1
1.0%
34 1
1.0%
38 1
1.0%
41 1
1.0%
ValueCountFrequency (%)
226 1
1.0%
225 1
1.0%
201 1
1.0%
171 1
1.0%
161 1
1.0%
157 1
1.0%
139 1
1.0%
138 1
1.0%
134 1
1.0%
123 1
1.0%

Interactions

2023-12-10T15:55:45.527709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.447969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.822989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.146737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.608978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.543134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.911756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.235502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.686821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.630272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.988599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.334691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.777610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:44.731010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.073686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:55:45.432846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:55:49.039519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
104156104.1세탁세제전체99경상남도전체.144
1041.0000.8781.0000.0000.0000.0000.3170.8180.831
1560.8781.0000.8780.0000.0000.0000.2710.7660.879
104.11.0000.8781.0000.0000.0000.0000.3170.8180.831
세탁세제0.0000.0000.0001.0000.9990.4710.0000.0000.220
전체0.0000.0000.0000.9991.0000.4710.0000.0000.220
990.0000.0000.0000.4710.4711.0000.3270.0000.283
경상남도0.3170.2710.3170.0000.0000.3271.0000.9720.300
전체.10.8180.7660.8180.0000.0000.0000.9721.0000.713
440.8310.8790.8310.2200.2200.2830.3000.7131.000
2023-12-10T15:55:49.158624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경상남도세탁세제전체.1전체99
경상남도1.0000.0000.6530.0000.235
세탁세제0.0001.0000.0000.9790.312
전체.10.6530.0001.0000.0000.000
전체0.0000.9790.0001.0000.312
990.2350.3120.0000.3121.000
2023-12-10T15:55:49.268399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
104156104.144세탁세제전체99경상남도전체.1
1041.0000.4641.0000.4870.0000.0000.0000.0850.270
1560.4641.0000.4640.7770.0000.0000.0000.1020.307
104.11.0000.4641.0000.4870.0000.0000.0000.0850.270
440.4870.7770.4871.0000.1590.1590.2060.1100.280
세탁세제0.0000.0000.0000.1591.0000.9790.3120.0000.000
전체0.0000.0000.0000.1590.9791.0000.3120.0000.000
990.0000.0000.0000.2060.3120.3121.0000.2350.000
경상남도0.0850.1020.0850.1100.0000.0000.2351.0000.653
전체.10.2700.3070.2700.2800.0000.0000.0000.6531.000

Missing values

2023-12-10T15:55:45.901872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:55:46.068407image/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

MT201901104156104.1세탁세제/섬유유연제세탁세제전체199경상남도전체.144
0MT201901118100118세탁세제/섬유유연제세탁세제세탁세제220전체전체86
1MT20190110567105세탁세제/섬유유연제세탁세제세탁세제220서울특별시도봉구74
2MT2019014810048세탁세제/섬유유연제세탁세제세탁세제220서울특별시양천구41
3MT201901182108182세탁세제/섬유유연제세탁세제세탁세제220서울특별시서초구88
4MT201901556255세탁세제/섬유유연제세탁세제세탁세제220서울특별시강남구51
5MT201901599559세탁세제/섬유유연제세탁세제세탁세제220부산광역시부산진구67
6MT20190110780107세탁세제/섬유유연제세탁세제세탁세제220부산광역시기장군58
7MT201901251439251세탁세제/섬유유연제세탁세제세탁세제220대구광역시중구201
8MT201901678367세탁세제/섬유유연제세탁세제세탁세제220대구광역시북구76
9MT2019018723587세탁세제/섬유유연제세탁세제세탁세제220대구광역시수성구138
MT201901104156104.1세탁세제/섬유유연제세탁세제전체199경상남도전체.144
89MT20190153853세탁세제/섬유유연제전체전체220충청남도보령시17
90MT20190110788107세탁세제/섬유유연제전체전체220전라북도전주시88
91MT201901797479세탁세제/섬유유연제전체전체220전라북도군산시84
92MT201901582558세탁세제/섬유유연제전체전체220전라남도나주시45
93MT201901192819세탁세제/섬유유연제전체전체220경상북도포항시20
94MT201901565356세탁세제/섬유유연제전체전체220경상북도경주시65
95MT201901532264532세탁세제/섬유유연제전체전체220경상북도성주군225
96MT20190111288112세탁세제/섬유유연제전체전체220경상남도전체76
97MT20190111492114세탁세제/섬유유연제전체전체220경상남도창원시81
98MT20190114385143세탁세제/섬유유연제전체전체220제주특별자치도전체74