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=ZTO011TOTALCGISPORT

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
스포츠/레저용품 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
2 has constant value ""Constant
109 is highly overall correlated with 90 and 2 other fieldsHigh correlation
90 is highly overall correlated with 109 and 2 other fieldsHigh correlation
109.1 is highly overall correlated with 109 and 2 other fieldsHigh correlation
65 is highly overall correlated with 109 and 2 other fieldsHigh correlation
전체.2 is highly overall correlated with 전체.3High correlation
전체.3 is highly overall correlated with 전체.2High correlation
109 has 1 (1.0%) zerosZeros
90 has 1 (1.0%) zerosZeros
109.1 has 1 (1.0%) zerosZeros
65 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:38:44.645994
Analysis finished2023-12-10 06:38:48.135461
Duration3.49 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

2023-12-10T15:38:48.236787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:48.392032image/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

2023-12-10T15:38:48.575710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:48.735130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

109
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.35354
Minimum0
Maximum1341
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:38:48.916429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q157
median85
Q3117.5
95-th percentile247.3
Maximum1341
Range1341
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation166.36494
Coefficient of variation (CV)1.382302
Kurtosis33.517975
Mean120.35354
Median Absolute Deviation (MAD)31
Skewness5.3239415
Sum11915
Variance27677.292
MonotonicityNot monotonic
2023-12-10T15:38:49.185162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 3
 
3.0%
57 3
 
3.0%
67 3
 
3.0%
89 3
 
3.0%
44 2
 
2.0%
42 2
 
2.0%
28 2
 
2.0%
112 2
 
2.0%
66 2
 
2.0%
59 2
 
2.0%
Other values (64) 75
75.8%
ValueCountFrequency (%)
0 1
1.0%
3 1
1.0%
13 1
1.0%
21 1
1.0%
28 2
2.0%
32 1
1.0%
34 1
1.0%
35 1
1.0%
37 2
2.0%
38 1
1.0%
ValueCountFrequency (%)
1341 1
1.0%
854 1
1.0%
635 1
1.0%
330 1
1.0%
277 1
1.0%
244 1
1.0%
243 1
1.0%
239 2
2.0%
231 1
1.0%
222 1
1.0%

90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.959596
Minimum0
Maximum388
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:38:49.433791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.9
Q160
median79
Q3100.5
95-th percentile176
Maximum388
Range388
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation57.268241
Coefficient of variation (CV)0.62960087
Kurtosis11.065862
Mean90.959596
Median Absolute Deviation (MAD)20
Skewness2.760799
Sum9005
Variance3279.6514
MonotonicityNot monotonic
2023-12-10T15:38:49.664407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 4
 
4.0%
75 3
 
3.0%
89 3
 
3.0%
81 3
 
3.0%
78 3
 
3.0%
67 2
 
2.0%
45 2
 
2.0%
69 2
 
2.0%
74 2
 
2.0%
84 2
 
2.0%
Other values (59) 73
73.7%
ValueCountFrequency (%)
0 1
1.0%
5 1
1.0%
16 2
2.0%
24 1
1.0%
25 1
1.0%
41 1
1.0%
45 2
2.0%
47 1
1.0%
48 1
1.0%
49 1
1.0%
ValueCountFrequency (%)
388 1
1.0%
354 1
1.0%
255 1
1.0%
213 1
1.0%
185 1
1.0%
175 1
1.0%
174 1
1.0%
162 1
1.0%
156 1
1.0%
147 1
1.0%

109.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.35354
Minimum0
Maximum1341
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:38:49.939432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q157
median85
Q3117.5
95-th percentile247.3
Maximum1341
Range1341
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation166.36494
Coefficient of variation (CV)1.382302
Kurtosis33.517975
Mean120.35354
Median Absolute Deviation (MAD)31
Skewness5.3239415
Sum11915
Variance27677.292
MonotonicityNot monotonic
2023-12-10T15:38:50.213786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 3
 
3.0%
57 3
 
3.0%
67 3
 
3.0%
89 3
 
3.0%
44 2
 
2.0%
42 2
 
2.0%
28 2
 
2.0%
112 2
 
2.0%
66 2
 
2.0%
59 2
 
2.0%
Other values (64) 75
75.8%
ValueCountFrequency (%)
0 1
1.0%
3 1
1.0%
13 1
1.0%
21 1
1.0%
28 2
2.0%
32 1
1.0%
34 1
1.0%
35 1
1.0%
37 2
2.0%
38 1
1.0%
ValueCountFrequency (%)
1341 1
1.0%
854 1
1.0%
635 1
1.0%
330 1
1.0%
277 1
1.0%
244 1
1.0%
243 1
1.0%
239 2
2.0%
231 1
1.0%
222 1
1.0%

스포츠/레저용품
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스포츠/레저용품
2nd row스포츠/레저용품
3rd row스포츠/레저용품
4th row스포츠/레저용품
5th row스포츠/레저용품

Common Values

ValueCountFrequency (%)
스포츠/레저용품 99
100.0%

Length

2023-12-10T15:38:50.434607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:50.565553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스포츠/레저용품 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

2023-12-10T15:38:50.690000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:50.841559image/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

2023-12-10T15:38:50.983818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:51.167627image/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

2023-12-10T15:38:51.320471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:51.504654image/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
20
50 
30
43 
40
6 

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 50
50.5%
30 43
43.4%
40 6
 
6.1%

Length

2023-12-10T15:38:51.658554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:38:51.815523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 50
50.5%
30 43
43.4%
40 6
 
6.1%

전체.2
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
서울특별시
26 
경기도
21 
부산광역시
8 
대구광역시
6 
광주광역시
6 
Other values (12)
32 

Length

Max length7
Median length5
Mean length4.3535354
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 26
26.3%
경기도 21
21.2%
부산광역시 8
 
8.1%
대구광역시 6
 
6.1%
광주광역시 6
 
6.1%
경상북도 5
 
5.1%
강원도 4
 
4.0%
울산광역시 4
 
4.0%
인천광역시 4
 
4.0%
경상남도 4
 
4.0%
Other values (7) 11
11.1%

Length

2023-12-10T15:38:51.985459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 26
26.3%
경기도 21
21.2%
부산광역시 8
 
8.1%
대구광역시 6
 
6.1%
광주광역시 6
 
6.1%
경상북도 5
 
5.1%
경상남도 4
 
4.0%
인천광역시 4
 
4.0%
울산광역시 4
 
4.0%
강원도 4
 
4.0%
Other values (7) 11
11.1%

전체.3
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
13 
북구
 
6
서구
 
4
동대문구
 
3
중구
 
3
Other values (38)
70 

Length

Max length4
Median length3
Mean length2.7878788
Min length2

Unique

Unique10 ?
Unique (%)10.1%

Sample

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

Common Values

ValueCountFrequency (%)
전체 13
 
13.1%
북구 6
 
6.1%
서구 4
 
4.0%
동대문구 3
 
3.0%
중구 3
 
3.0%
영등포구 3
 
3.0%
강북구 3
 
3.0%
은평구 3
 
3.0%
금천구 3
 
3.0%
기장군 2
 
2.0%
Other values (33) 56
56.6%

Length

2023-12-10T15:38:52.302915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 13
 
13.1%
북구 6
 
6.1%
서구 4
 
4.0%
동대문구 3
 
3.0%
중구 3
 
3.0%
영등포구 3
 
3.0%
강북구 3
 
3.0%
은평구 3
 
3.0%
금천구 3
 
3.0%
동구 2
 
2.0%
Other values (33) 56
56.6%

65
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.757576
Minimum0
Maximum213
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:38:52.590275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.9
Q136.5
median51
Q369
95-th percentile103.3
Maximum213
Range213
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation32.047506
Coefficient of variation (CV)0.57476506
Kurtosis8.2173746
Mean55.757576
Median Absolute Deviation (MAD)17
Skewness2.0977842
Sum5520
Variance1027.0427
MonotonicityNot monotonic
2023-12-10T15:38:52.849337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 6
 
6.1%
46 5
 
5.1%
41 4
 
4.0%
53 4
 
4.0%
32 4
 
4.0%
59 3
 
3.0%
31 3
 
3.0%
51 3
 
3.0%
80 2
 
2.0%
27 2
 
2.0%
Other values (47) 63
63.6%
ValueCountFrequency (%)
0 1
1.0%
1 1
1.0%
7 1
1.0%
10 1
1.0%
13 1
1.0%
14 1
1.0%
21 1
1.0%
24 1
1.0%
26 2
2.0%
27 2
2.0%
ValueCountFrequency (%)
213 1
1.0%
196 1
1.0%
128 1
1.0%
108 1
1.0%
106 1
1.0%
103 1
1.0%
98 1
1.0%
91 1
1.0%
87 1
1.0%
85 1
1.0%

Interactions

2023-12-10T15:38:47.151380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.149541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.671336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:46.548614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:47.271451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.265623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.804825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:46.682458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:47.393071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.384328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.938418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:46.867024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:47.541085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:45.532951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:46.085379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:38:47.015026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:38:53.074399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10990109.120전체.2전체.365
1091.0000.8431.0000.5110.0000.2590.822
900.8431.0000.8430.3920.0000.3110.944
109.11.0000.8431.0000.5110.0000.2590.822
200.5110.3920.5111.0000.0000.0000.358
전체.20.0000.0000.0000.0001.0000.9440.000
전체.30.2590.3110.2590.0000.9441.0000.667
650.8220.9440.8220.3580.0000.6671.000
2023-12-10T15:38:53.254523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20전체.3전체.2
201.0000.0000.000
전체.30.0001.0000.502
전체.20.0000.5021.000
2023-12-10T15:38:53.402897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10990109.16520전체.2전체.3
1091.0000.5941.0000.6500.2400.0000.058
900.5941.0000.5940.6860.2630.0000.070
109.11.0000.5941.0000.6500.2400.0000.058
650.6500.6860.6501.0000.2360.0000.248
200.2400.2630.2400.2361.0000.0000.000
전체.20.0000.0000.0000.0000.0001.0000.502
전체.30.0580.0700.0580.2480.0000.5021.000

Missing values

2023-12-10T15:38:47.728209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:38:48.033212image/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

MT20190110990109.1스포츠/레저용품전체전체.1220전체.2전체.365
0MT20190113411621341스포츠/레저용품전체전체220서울특별시도봉구196
1MT201901639963스포츠/레저용품전체전체220서울특별시양천구21
2MT201901116111116스포츠/레저용품전체전체220서울특별시동작구84
3MT201901508650스포츠/레저용품전체전체220서울특별시서초구59
4MT201901347134스포츠/레저용품전체전체220서울특별시강남구62
5MT201901203388203스포츠/레저용품전체전체220부산광역시부산진구69
6MT20190114364143스포츠/레저용품전체전체220부산광역시기장군31
7MT201901321632스포츠/레저용품전체전체220대구광역시중구39
8MT201901125111125스포츠/레저용품전체전체220대구광역시북구85
9MT201901735773스포츠/레저용품전체전체220인천광역시중구51
MT20190110990109.1스포츠/레저용품전체전체.1220전체.2전체.365
89MT201901669866스포츠/레저용품전체전체230충청북도전체51
90MT20190110480104스포츠/레저용품전체전체230경상북도전체62
91MT201901374837스포츠/레저용품전체전체230경상북도김천시35
92MT201901424742스포츠/레저용품전체전체230경상남도통영시32
93MT201901808080스포츠/레저용품전체전체240서울특별시동대문구32
94MT201901965996스포츠/레저용품전체전체240서울특별시강북구43
95MT201901719071스포츠/레저용품전체전체240서울특별시은평구45
96MT201901000스포츠/레저용품전체전체240서울특별시서대문구0
97MT201901635255635스포츠/레저용품전체전체240서울특별시금천구213
98MT2019018110981스포츠/레저용품전체전체240서울특별시영등포구63