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=ZTO011TOTALCGILAPTO

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
노트북/태블릿 has constant value ""Constant
전체.1 has constant value ""Constant
2 has constant value ""Constant
155 is highly overall correlated with 162 and 2 other fieldsHigh correlation
162 is highly overall correlated with 155 and 2 other fieldsHigh correlation
155.1 is highly overall correlated with 155 and 2 other fieldsHigh correlation
130 is highly overall correlated with 155 and 2 other fieldsHigh correlation
전체 is highly overall correlated with 20High correlation
20 is highly overall correlated with 전체High correlation
전체.2 is highly overall correlated with 전체.3High correlation
전체.3 is highly overall correlated with 전체.2High correlation

Reproduction

Analysis started2023-12-10 06:16:23.777959
Analysis finished2023-12-10 06:16:27.375842
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

2023-12-10T15:16:27.494349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:27.669499image/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

2023-12-10T15:16:27.818078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:27.991239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

155
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.0404
Minimum15
Maximum1697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:16:28.178651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile32.7
Q176
median122
Q3168
95-th percentile363.3
Maximum1697
Range1682
Interquartile range (IQR)92

Descriptive statistics

Standard deviation230.69929
Coefficient of variation (CV)1.3409599
Kurtosis27.399477
Mean172.0404
Median Absolute Deviation (MAD)46
Skewness4.8753688
Sum17032
Variance53222.162
MonotonicityNot monotonic
2023-12-10T15:16:28.401937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 4
 
4.0%
129 3
 
3.0%
144 3
 
3.0%
115 2
 
2.0%
168 2
 
2.0%
138 2
 
2.0%
81 2
 
2.0%
97 2
 
2.0%
170 2
 
2.0%
101 2
 
2.0%
Other values (71) 75
75.8%
ValueCountFrequency (%)
15 1
1.0%
18 1
1.0%
24 1
1.0%
28 1
1.0%
30 1
1.0%
33 1
1.0%
34 1
1.0%
38 1
1.0%
39 1
1.0%
40 1
1.0%
ValueCountFrequency (%)
1697 1
1.0%
1414 1
1.0%
735 1
1.0%
628 1
1.0%
555 1
1.0%
342 1
1.0%
321 1
1.0%
311 1
1.0%
298 1
1.0%
272 1
1.0%

162
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.91919
Minimum11
Maximum1118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:16:28.645970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile46.8
Q194
median141
Q3209
95-th percentile481.4
Maximum1118
Range1107
Interquartile range (IQR)115

Descriptive statistics

Standard deviation169.37735
Coefficient of variation (CV)0.90615281
Kurtosis11.501164
Mean186.91919
Median Absolute Deviation (MAD)57
Skewness2.9524061
Sum18505
Variance28688.687
MonotonicityNot monotonic
2023-12-10T15:16:28.875804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129 5
 
5.1%
48 3
 
3.0%
66 2
 
2.0%
102 2
 
2.0%
83 2
 
2.0%
210 2
 
2.0%
162 2
 
2.0%
99 2
 
2.0%
141 2
 
2.0%
178 2
 
2.0%
Other values (74) 75
75.8%
ValueCountFrequency (%)
11 1
 
1.0%
29 1
 
1.0%
40 1
 
1.0%
42 1
 
1.0%
45 1
 
1.0%
47 1
 
1.0%
48 3
3.0%
51 1
 
1.0%
60 1
 
1.0%
62 1
 
1.0%
ValueCountFrequency (%)
1118 1
1.0%
887 1
1.0%
647 1
1.0%
615 1
1.0%
521 1
1.0%
477 1
1.0%
412 1
1.0%
394 1
1.0%
380 1
1.0%
363 1
1.0%

155.1
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.0404
Minimum15
Maximum1697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:16:29.096814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile32.7
Q176
median122
Q3168
95-th percentile363.3
Maximum1697
Range1682
Interquartile range (IQR)92

Descriptive statistics

Standard deviation230.69929
Coefficient of variation (CV)1.3409599
Kurtosis27.399477
Mean172.0404
Median Absolute Deviation (MAD)46
Skewness4.8753688
Sum17032
Variance53222.162
MonotonicityNot monotonic
2023-12-10T15:16:29.330491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 4
 
4.0%
129 3
 
3.0%
144 3
 
3.0%
115 2
 
2.0%
168 2
 
2.0%
138 2
 
2.0%
81 2
 
2.0%
97 2
 
2.0%
170 2
 
2.0%
101 2
 
2.0%
Other values (71) 75
75.8%
ValueCountFrequency (%)
15 1
1.0%
18 1
1.0%
24 1
1.0%
28 1
1.0%
30 1
1.0%
33 1
1.0%
34 1
1.0%
38 1
1.0%
39 1
1.0%
40 1
1.0%
ValueCountFrequency (%)
1697 1
1.0%
1414 1
1.0%
735 1
1.0%
628 1
1.0%
555 1
1.0%
342 1
1.0%
321 1
1.0%
311 1
1.0%
298 1
1.0%
272 1
1.0%

노트북/태블릿
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노트북/태블릿
2nd row노트북/태블릿
3rd row노트북/태블릿
4th row노트북/태블릿
5th row노트북/태블릿

Common Values

ValueCountFrequency (%)
노트북/태블릿 99
100.0%

Length

2023-12-10T15:16:29.652491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:29.794115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노트북/태블릿 99
100.0%

전체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
63 
노트북
36 

Length

Max length3
Median length2
Mean length2.3636364
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 63
63.6%
노트북 36
36.4%

Length

2023-12-10T15:16:29.946078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:30.116977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 63
63.6%
노트북 36
36.4%

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

2023-12-10T15:16:30.288884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:30.435913image/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

2023-12-10T15:16:30.575722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:30.708575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 99
100.0%

20
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
30
47 
20
34 
40
18 

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 47
47.5%
20 34
34.3%
40 18
 
18.2%

Length

2023-12-10T15:16:30.869698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:31.062842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 47
47.5%
20 34
34.3%
40 18
 
18.2%

전체.2
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
22 
서울특별시
15 
인천광역시
9 
광주광역시
9 
대구광역시
6 
Other values (10)
38 

Length

Max length7
Median length5
Mean length4.3636364
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 22
22.2%
서울특별시 15
15.2%
인천광역시 9
9.1%
광주광역시 9
9.1%
대구광역시 6
 
6.1%
울산광역시 6
 
6.1%
강원도 6
 
6.1%
부산광역시 5
 
5.1%
전라북도 4
 
4.0%
경상남도 4
 
4.0%
Other values (5) 13
13.1%

Length

2023-12-10T15:16:31.245902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 22
22.2%
서울특별시 15
15.2%
인천광역시 9
9.1%
광주광역시 9
9.1%
대구광역시 6
 
6.1%
울산광역시 6
 
6.1%
강원도 6
 
6.1%
부산광역시 5
 
5.1%
전라북도 4
 
4.0%
경상남도 4
 
4.0%
Other values (5) 13
13.1%

전체.3
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
11 
북구
9 
서구
 
6
화성시
 
3
남동구
 
3
Other values (27)
67 

Length

Max length4
Median length3
Mean length2.7373737
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 11
 
11.1%
북구 9
 
9.1%
서구 6
 
6.1%
화성시 3
 
3.0%
남동구 3
 
3.0%
동작구 3
 
3.0%
서초구 3
 
3.0%
부산진구 3
 
3.0%
수성구 3
 
3.0%
중구 3
 
3.0%
Other values (22) 52
52.5%

Length

2023-12-10T15:16:31.470691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 11
 
11.1%
북구 9
 
9.1%
서구 6
 
6.1%
강남구 3
 
3.0%
구리시 3
 
3.0%
도봉구 3
 
3.0%
원주시 3
 
3.0%
광주시 3
 
3.0%
양천구 3
 
3.0%
파주시 3
 
3.0%
Other values (22) 52
52.5%

130
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.06061
Minimum16
Maximum318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:16:31.697124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile43.7
Q187
median120
Q3155.5
95-th percentile218
Maximum318
Range302
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation56.683298
Coefficient of variation (CV)0.45324663
Kurtosis1.4402767
Mean125.06061
Median Absolute Deviation (MAD)35
Skewness0.85590282
Sum12381
Variance3212.9963
MonotonicityNot monotonic
2023-12-10T15:16:31.928846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 5
 
5.1%
108 4
 
4.0%
111 3
 
3.0%
66 3
 
3.0%
137 3
 
3.0%
150 2
 
2.0%
143 2
 
2.0%
161 2
 
2.0%
158 2
 
2.0%
120 2
 
2.0%
Other values (63) 71
71.7%
ValueCountFrequency (%)
16 1
 
1.0%
29 1
 
1.0%
36 1
 
1.0%
38 1
 
1.0%
41 1
 
1.0%
44 1
 
1.0%
54 1
 
1.0%
56 1
 
1.0%
59 1
 
1.0%
60 5
5.1%
ValueCountFrequency (%)
318 1
1.0%
294 1
1.0%
276 1
1.0%
275 1
1.0%
227 1
1.0%
217 1
1.0%
207 1
1.0%
206 1
1.0%
204 1
1.0%
197 1
1.0%

Interactions

2023-12-10T15:16:26.033795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:24.393710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:24.980174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.550980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:26.170954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:24.541079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.129460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.686287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:26.303543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:24.721903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.266178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.798229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:26.430196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:24.843917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.416268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.912207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:16:32.080945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
155162155.1전체20전체.2전체.3130
1551.0000.5811.0000.0000.3560.0000.2420.355
1620.5811.0000.5810.2020.3850.3670.7190.583
155.11.0000.5811.0000.0000.3560.0000.2420.355
전체0.0000.2020.0001.0000.4600.0000.0000.000
200.3560.3850.3560.4601.0000.0000.0000.244
전체.20.0000.3670.0000.0000.0001.0000.9700.357
전체.30.2420.7190.2420.0000.0000.9701.0000.470
1300.3550.5830.3550.0000.2440.3570.4701.000
2023-12-10T15:16:32.229376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체전체.320전체.2
전체1.0000.0000.7070.000
전체.30.0001.0000.0000.679
200.7070.0001.0000.000
전체.20.0000.6790.0001.000
2023-12-10T15:16:32.369465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
155162155.1130전체20전체.2전체.3
1551.0000.5441.0000.5670.0000.1540.0000.072
1620.5441.0000.5440.6240.1440.2570.1550.290
155.11.0000.5441.0000.5670.0000.1540.0000.072
1300.5670.6240.5671.0000.0000.1030.1430.157
전체0.0000.1440.0000.0001.0000.7070.0000.000
200.1540.2570.1540.1030.7071.0000.0000.000
전체.20.0000.1550.0000.1430.0000.0001.0000.679
전체.30.0720.2900.0720.1570.0000.0000.6791.000

Missing values

2023-12-10T15:16:26.608685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:16:27.272278image/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

MT201901155162155.1노트북/태블릿전체전체.1220전체.2전체.3130
0MT20190116971771697노트북/태블릿전체전체220서울특별시도봉구122
1MT2019013413734노트북/태블릿전체전체220서울특별시양천구64
2MT201901166412166노트북/태블릿전체전체220서울특별시동작구197
3MT20190129883298노트북/태블릿전체전체220서울특별시서초구60
4MT20190111442114노트북/태블릿전체전체220서울특별시강남구60
5MT201901396839노트북/태블릿전체전체220부산광역시부산진구36
6MT201901233129233노트북/태블릿전체전체220대구광역시북구136
7MT2019014514745노트북/태블릿전체전체220대구광역시수성구88
8MT201901404740노트북/태블릿전체전체220인천광역시중구60
9MT201901142363142노트북/태블릿전체전체220인천광역시남동구155
MT201901155162155.1노트북/태블릿전체전체.1220전체.2전체.3130
89MT2019018120881노트북/태블릿노트북전체240대구광역시북구83
90MT201901101310101노트북/태블릿노트북전체240대구광역시수성구143
91MT201901232887232노트북/태블릿노트북전체240인천광역시중구227
92MT201901628647628노트북/태블릿노트북전체240인천광역시남동구217
93MT201901143115143노트북/태블릿노트북전체240인천광역시서구106
94MT201901164304164노트북/태블릿노트북전체240광주광역시서구119
95MT201901481148노트북/태블릿노트북전체240광주광역시북구16
96MT201901129210129노트북/태블릿노트북전체240광주광역시광산구126
97MT201901179253179노트북/태블릿노트북전체240울산광역시전체206
98MT201901170334170노트북/태블릿노트북전체240울산광역시북구275