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=ZTO011TOTALCGIDIAPER

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
기저귀 has constant value ""Constant
전체 has constant value ""Constant
전체.1 has constant value ""Constant
2 has constant value ""Constant
159 is highly overall correlated with 159.1High correlation
159.1 is highly overall correlated with 159High correlation
전체.2 is highly overall correlated with 전체.3High correlation
전체.3 is highly overall correlated with 전체.2High correlation
159 has 1 (1.0%) zerosZeros
104 has 1 (1.0%) zerosZeros
159.1 has 1 (1.0%) zerosZeros
78 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:42:14.843039
Analysis finished2023-12-10 06:42:18.438287
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

2023-12-10T15:42:18.549991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:18.678370image/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

2023-12-10T15:42:18.816502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:18.960507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

159
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.24242
Minimum-541
Maximum633
Zeros1
Zeros (%)1.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:42:19.124093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-541
5-th percentile19.8
Q184
median127
Q3175.5
95-th percentile279.3
Maximum633
Range1174
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation116.10622
Coefficient of variation (CV)0.87798014
Kurtosis14.71375
Mean132.24242
Median Absolute Deviation (MAD)45
Skewness-0.68642463
Sum13092
Variance13480.655
MonotonicityNot monotonic
2023-12-10T15:42:19.360720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 3
 
3.0%
127 3
 
3.0%
95 2
 
2.0%
268 2
 
2.0%
98 2
 
2.0%
138 2
 
2.0%
73 2
 
2.0%
38 2
 
2.0%
190 2
 
2.0%
181 2
 
2.0%
Other values (70) 77
77.8%
ValueCountFrequency (%)
-541 1
1.0%
0 1
1.0%
8 1
1.0%
14 1
1.0%
18 1
1.0%
20 1
1.0%
28 1
1.0%
33 1
1.0%
38 2
2.0%
42 1
1.0%
ValueCountFrequency (%)
633 1
1.0%
517 1
1.0%
337 1
1.0%
330 1
1.0%
318 1
1.0%
275 1
1.0%
268 2
2.0%
249 1
1.0%
241 1
1.0%
234 1
1.0%

104
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8063469.7
Minimum-7.9829811 × 108
Maximum2578
Zeros1
Zeros (%)1.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:42:19.638322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.9829811 × 108
5-th percentile16.4
Q179.5
median107
Q3132.5
95-th percentile279.3
Maximum2578
Range7.9830069 × 108
Interquartile range (IQR)53

Descriptive statistics

Standard deviation80231994
Coefficient of variation (CV)-9.9500584
Kurtosis99
Mean-8063469.7
Median Absolute Deviation (MAD)27
Skewness-9.9498744
Sum-7.982835 × 108
Variance6.4371729 × 1015
MonotonicityNot monotonic
2023-12-10T15:42:19.882434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 3
 
3.0%
127 3
 
3.0%
117 3
 
3.0%
135 3
 
3.0%
90 2
 
2.0%
62 2
 
2.0%
97 2
 
2.0%
105 2
 
2.0%
128 2
 
2.0%
110 2
 
2.0%
Other values (67) 75
75.8%
ValueCountFrequency (%)
-798298112 1
1.0%
0 1
1.0%
9 1
1.0%
11 2
2.0%
17 1
1.0%
21 1
1.0%
44 1
1.0%
45 2
2.0%
49 1
1.0%
50 1
1.0%
ValueCountFrequency (%)
2578 1
1.0%
908 1
1.0%
592 1
1.0%
338 1
1.0%
336 1
1.0%
273 1
1.0%
271 1
1.0%
227 1
1.0%
222 1
1.0%
194 1
1.0%

159.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.24242
Minimum-541
Maximum633
Zeros1
Zeros (%)1.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:42:20.141324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-541
5-th percentile19.8
Q184
median127
Q3175.5
95-th percentile279.3
Maximum633
Range1174
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation116.10622
Coefficient of variation (CV)0.87798014
Kurtosis14.71375
Mean132.24242
Median Absolute Deviation (MAD)45
Skewness-0.68642463
Sum13092
Variance13480.655
MonotonicityNot monotonic
2023-12-10T15:42:20.410208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 3
 
3.0%
127 3
 
3.0%
95 2
 
2.0%
268 2
 
2.0%
98 2
 
2.0%
138 2
 
2.0%
73 2
 
2.0%
38 2
 
2.0%
190 2
 
2.0%
181 2
 
2.0%
Other values (70) 77
77.8%
ValueCountFrequency (%)
-541 1
1.0%
0 1
1.0%
8 1
1.0%
14 1
1.0%
18 1
1.0%
20 1
1.0%
28 1
1.0%
33 1
1.0%
38 2
2.0%
42 1
1.0%
ValueCountFrequency (%)
633 1
1.0%
517 1
1.0%
337 1
1.0%
330 1
1.0%
318 1
1.0%
275 1
1.0%
268 2
2.0%
249 1
1.0%
241 1
1.0%
234 1
1.0%

기저귀
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기저귀 99
100.0%

Length

2023-12-10T15:42:20.632802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:20.797714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기저귀 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

2023-12-10T15:42:20.963807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:21.113801image/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

2023-12-10T15:42:21.309894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:21.467455image/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

2023-12-10T15:42:21.664573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:21.822690image/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
44 
20
34 
40
21 

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 44
44.4%
20 34
34.3%
40 21
21.2%

Length

2023-12-10T15:42:21.965110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:22.138855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 44
44.4%
20 34
34.3%
40 21
21.2%

전체.2
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
13 
서울특별시
11 
대구광역시
9 
인천광역시
9 
광주광역시
9 
Other values (11)
48 

Length

Max length7
Median length5
Mean length4.4848485
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 13
13.1%
서울특별시 11
11.1%
대구광역시 9
9.1%
인천광역시 9
9.1%
광주광역시 9
9.1%
울산광역시 8
8.1%
강원도 8
8.1%
부산광역시 6
 
6.1%
전라북도 4
 
4.0%
경상북도 4
 
4.0%
Other values (6) 18
18.2%

Length

2023-12-10T15:42:22.318810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 13
13.1%
서울특별시 11
11.1%
대구광역시 9
9.1%
인천광역시 9
9.1%
광주광역시 9
9.1%
울산광역시 8
8.1%
강원도 8
8.1%
부산광역시 6
 
6.1%
전라북도 4
 
4.0%
경상북도 4
 
4.0%
Other values (6) 18
18.2%

전체.3
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
12 
북구
9 
서구
 
6
중구
 
6
수성구
 
3
Other values (31)
63 

Length

Max length4
Median length3
Mean length2.6969697
Min length2

Unique

Unique6 ?
Unique (%)6.1%

Sample

1st row도봉구
2nd row서초구
3rd row강남구
4th row부산진구
5th row기장군

Common Values

ValueCountFrequency (%)
전체 12
 
12.1%
북구 9
 
9.1%
서구 6
 
6.1%
중구 6
 
6.1%
수성구 3
 
3.0%
강남구 3
 
3.0%
기장군 3
 
3.0%
부산진구 3
 
3.0%
광산구 3
 
3.0%
서초구 3
 
3.0%
Other values (26) 48
48.5%

Length

2023-12-10T15:42:22.551180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 12
 
12.1%
북구 9
 
9.1%
서구 6
 
6.1%
중구 6
 
6.1%
광산구 3
 
3.0%
도봉구 3
 
3.0%
서초구 3
 
3.0%
남동구 3
 
3.0%
부산진구 3
 
3.0%
기장군 3
 
3.0%
Other values (26) 48
48.5%

78
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.45455
Minimum0
Maximum479
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:42:22.772144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.5
Q174.5
median100
Q3116
95-th percentile227.1
Maximum479
Range479
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation64.961727
Coefficient of variation (CV)0.60455076
Kurtosis11.752961
Mean107.45455
Median Absolute Deviation (MAD)20
Skewness2.7459656
Sum10638
Variance4220.026
MonotonicityNot monotonic
2023-12-10T15:42:22.993649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116 4
 
4.0%
102 4
 
4.0%
84 3
 
3.0%
95 3
 
3.0%
96 3
 
3.0%
105 3
 
3.0%
104 3
 
3.0%
90 3
 
3.0%
61 2
 
2.0%
71 2
 
2.0%
Other values (60) 69
69.7%
ValueCountFrequency (%)
0 1
1.0%
21 2
2.0%
25 1
1.0%
26 1
1.0%
31 1
1.0%
43 1
1.0%
46 1
1.0%
52 2
2.0%
56 1
1.0%
57 2
2.0%
ValueCountFrequency (%)
479 1
1.0%
338 1
1.0%
273 1
1.0%
253 1
1.0%
237 1
1.0%
226 2
2.0%
188 1
1.0%
185 1
1.0%
181 1
1.0%
169 1
1.0%

Interactions

2023-12-10T15:42:17.455796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:15.455990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:15.975357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:16.926838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:17.583585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:15.584553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:16.465806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:17.060479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:17.739922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:15.737379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:16.639587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:17.202172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:17.860902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:15.851451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:16.798771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:17.336626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:42:23.164047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
159104159.120전체.2전체.378
1591.000NaN1.0000.2450.3400.0000.283
104NaN1.000NaNNaNNaNNaNNaN
159.11.000NaN1.0000.2450.3400.0000.283
200.245NaN0.2451.0000.0000.0000.287
전체.20.340NaN0.3400.0001.0000.9740.445
전체.30.000NaN0.0000.0000.9741.0000.597
780.283NaN0.2830.2870.4450.5971.000
2023-12-10T15:42:23.341111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체.220전체.3
전체.21.0000.0000.663
200.0001.0000.000
전체.30.6630.0001.000
2023-12-10T15:42:23.444627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
159104159.17820전체.2전체.3
1591.0000.4761.0000.0960.2080.1080.000
1040.4761.0000.4760.4610.0000.0000.373
159.11.0000.4761.0000.0960.2080.1080.000
780.0960.4610.0961.0000.1830.1580.219
200.2080.0000.2080.1831.0000.0000.000
전체.20.1080.0000.1080.1580.0001.0000.663
전체.30.0000.3730.0000.2190.0000.6631.000

Missing values

2023-12-10T15:42:18.053108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:42:18.336107image/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

MT201901159104159.1기저귀전체전체.1220전체.2전체.378
0MT201901110271110기저귀전체전체220서울특별시도봉구75
1MT20190119079190기저귀전체전체220서울특별시서초구65
2MT201901176120176기저귀전체전체220서울특별시강남구137
3MT201901632163기저귀전체전체220부산광역시부산진구25
4MT201901241134241기저귀전체전체220부산광역시기장군71
5MT201901831183기저귀전체전체220대구광역시중구21
6MT20190113756137기저귀전체전체220대구광역시북구31
7MT201901128908128기저귀전체전체220대구광역시수성구147
8MT201901275222275기저귀전체전체220인천광역시중구84
9MT201901181273181기저귀전체전체220인천광역시남동구338
MT201901159104159.1기저귀전체전체.1220전체.2전체.378
89MT201901184131184기저귀전체전체240인천광역시남동구479
90MT201901129114129기저귀전체전체240인천광역시서구112
91MT201901115142115기저귀전체전체240광주광역시서구115
92MT201901498549기저귀전체전체240광주광역시북구121
93MT201901120125120기저귀전체전체240광주광역시광산구124
94MT201901128117128기저귀전체전체240울산광역시전체101
95MT201901100102100기저귀전체전체240울산광역시북구95
96MT2019017733677기저귀전체전체240울산광역시울주군273
97MT201901633227633기저귀전체전체240세종특별자치시전체188
98MT201901825782기저귀전체전체240경기도광명시139