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=ZTO011TOTALCGIPROCE

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
가공유 has constant value ""Constant
전체.1 has constant value ""Constant
전체 is highly overall correlated with 1High correlation
1 is highly overall correlated with 전체High correlation
111 is highly overall correlated with 111.1High correlation
114 is highly overall correlated with 74High correlation
111.1 is highly overall correlated with 111High correlation
74 is highly overall correlated with 114High correlation
전라남도 is highly overall correlated with 나주시High correlation
나주시 is highly overall correlated with 전라남도High correlation
114 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:43:49.346634
Analysis finished2023-12-10 06:43:53.049342
Duration3.7 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

2023-12-10T15:43:53.135770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:53.286612image/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

2023-12-10T15:43:53.428642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:53.571442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

111
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.28283
Minimum-977
Maximum2073
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:43:53.719440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-977
5-th percentile14.6
Q175.5
median108
Q3141
95-th percentile284.2
Maximum2073
Range3050
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation262.21731
Coefficient of variation (CV)1.8962392
Kurtosis35.878457
Mean138.28283
Median Absolute Deviation (MAD)33
Skewness4.1469064
Sum13690
Variance68757.919
MonotonicityNot monotonic
2023-12-10T15:43:53.911094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121 3
 
3.0%
108 3
 
3.0%
125 2
 
2.0%
92 2
 
2.0%
11 2
 
2.0%
160 2
 
2.0%
104 2
 
2.0%
36 2
 
2.0%
33 2
 
2.0%
86 2
 
2.0%
Other values (73) 77
77.8%
ValueCountFrequency (%)
-977 1
1.0%
2 1
1.0%
6 1
1.0%
11 2
2.0%
15 1
1.0%
33 2
2.0%
36 2
2.0%
41 1
1.0%
43 1
1.0%
47 1
1.0%
ValueCountFrequency (%)
2073 1
1.0%
1228 1
1.0%
420 1
1.0%
381 1
1.0%
349 1
1.0%
277 1
1.0%
274 1
1.0%
255 1
1.0%
238 1
1.0%
226 1
1.0%

114
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.17172
Minimum0
Maximum400
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:43:54.109760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.6
Q168
median91
Q3121.5
95-th percentile224.6
Maximum400
Range400
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation64.332053
Coefficient of variation (CV)0.61755777
Kurtosis5.8374564
Mean104.17172
Median Absolute Deviation (MAD)24
Skewness2.0068446
Sum10313
Variance4138.6131
MonotonicityNot monotonic
2023-12-10T15:43:54.366813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92 5
 
5.1%
88 4
 
4.0%
67 3
 
3.0%
91 3
 
3.0%
85 3
 
3.0%
114 2
 
2.0%
110 2
 
2.0%
74 2
 
2.0%
109 2
 
2.0%
56 2
 
2.0%
Other values (61) 71
71.7%
ValueCountFrequency (%)
0 1
1.0%
8 1
1.0%
9 1
1.0%
12 1
1.0%
17 1
1.0%
21 1
1.0%
32 1
1.0%
42 1
1.0%
47 1
1.0%
48 1
1.0%
ValueCountFrequency (%)
400 1
1.0%
326 1
1.0%
302 1
1.0%
272 1
1.0%
257 1
1.0%
221 1
1.0%
209 1
1.0%
206 1
1.0%
199 1
1.0%
168 1
1.0%

111.1
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.28283
Minimum-977
Maximum2073
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1023.0 B
2023-12-10T15:43:54.606523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-977
5-th percentile14.6
Q175.5
median108
Q3141
95-th percentile284.2
Maximum2073
Range3050
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation262.21731
Coefficient of variation (CV)1.8962392
Kurtosis35.878457
Mean138.28283
Median Absolute Deviation (MAD)33
Skewness4.1469064
Sum13690
Variance68757.919
MonotonicityNot monotonic
2023-12-10T15:43:54.800772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121 3
 
3.0%
108 3
 
3.0%
125 2
 
2.0%
92 2
 
2.0%
11 2
 
2.0%
160 2
 
2.0%
104 2
 
2.0%
36 2
 
2.0%
33 2
 
2.0%
86 2
 
2.0%
Other values (73) 77
77.8%
ValueCountFrequency (%)
-977 1
1.0%
2 1
1.0%
6 1
1.0%
11 2
2.0%
15 1
1.0%
33 2
2.0%
36 2
2.0%
41 1
1.0%
43 1
1.0%
47 1
1.0%
ValueCountFrequency (%)
2073 1
1.0%
1228 1
1.0%
420 1
1.0%
381 1
1.0%
349 1
1.0%
277 1
1.0%
274 1
1.0%
255 1
1.0%
238 1
1.0%
226 1
1.0%

가공유
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가공유 99
100.0%

Length

2023-12-10T15:43:54.965131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:55.092067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가공유 99
100.0%

전체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
65 
딸기우유
34 

Length

Max length4
Median length2
Mean length2.6868687
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 65
65.7%
딸기우유 34
34.3%

Length

2023-12-10T15:43:55.278136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:55.471105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 65
65.7%
딸기우유 34
34.3%

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

2023-12-10T15:43:55.648480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:55.785188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 99
100.0%

1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
1
65 
2
34 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 65
65.7%
2 34
34.3%

Length

2023-12-10T15:43:55.903289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:56.405014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 65
65.7%
2 34
34.3%

50
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
60
82 
50
9 
99
 
8

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
60 82
82.8%
50 9
 
9.1%
99 8
 
8.1%

Length

2023-12-10T15:43:56.595711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:43:56.741467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 82
82.8%
50 9
 
9.1%
99 8
 
8.1%

전라남도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
18 
서울특별시
14 
경상북도
8 
강원도
8 
부산광역시
8 
Other values (12)
43 

Length

Max length7
Median length5
Mean length4.3535354
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 18
18.2%
서울특별시 14
14.1%
경상북도 8
8.1%
강원도 8
8.1%
부산광역시 8
8.1%
대구광역시 7
 
7.1%
광주광역시 6
 
6.1%
인천광역시 6
 
6.1%
울산광역시 6
 
6.1%
경상남도 4
 
4.0%
Other values (7) 14
14.1%

Length

2023-12-10T15:43:56.931027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 18
18.2%
서울특별시 14
14.1%
경상북도 8
8.1%
강원도 8
8.1%
부산광역시 8
8.1%
대구광역시 7
 
7.1%
인천광역시 6
 
6.1%
울산광역시 6
 
6.1%
광주광역시 6
 
6.1%
경상남도 4
 
4.0%
Other values (7) 14
14.1%

나주시
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
10 
서구
 
6
북구
 
6
중구
 
4
서초구
 
3
Other values (36)
70 

Length

Max length4
Median length3
Mean length2.7676768
Min length2

Unique

Unique8 ?
Unique (%)8.1%

Sample

1st row포항시
2nd row경주시
3rd row안동시
4th row문경시
5th row성주군

Common Values

ValueCountFrequency (%)
전체 10
 
10.1%
서구 6
 
6.1%
북구 6
 
6.1%
중구 4
 
4.0%
서초구 3
 
3.0%
부산진구 3
 
3.0%
수성구 3
 
3.0%
양천구 3
 
3.0%
동작구 3
 
3.0%
기장군 3
 
3.0%
Other values (31) 55
55.6%

Length

2023-12-10T15:43:57.184524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 10
 
10.1%
북구 6
 
6.1%
서구 6
 
6.1%
중구 4
 
4.0%
서초구 3
 
3.0%
부산진구 3
 
3.0%
수성구 3
 
3.0%
양천구 3
 
3.0%
동작구 3
 
3.0%
기장군 3
 
3.0%
Other values (31) 55
55.6%

74
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.89899
Minimum4
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:43:57.417942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile21
Q171.5
median88
Q399.5
95-th percentile127.4
Maximum222
Range218
Interquartile range (IQR)28

Descriptive statistics

Standard deviation33.617141
Coefficient of variation (CV)0.40068588
Kurtosis2.8443785
Mean83.89899
Median Absolute Deviation (MAD)15
Skewness0.30397541
Sum8306
Variance1130.1121
MonotonicityNot monotonic
2023-12-10T15:43:57.643089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 4
 
4.0%
99 4
 
4.0%
90 3
 
3.0%
79 3
 
3.0%
87 3
 
3.0%
88 3
 
3.0%
95 3
 
3.0%
101 3
 
3.0%
86 3
 
3.0%
106 3
 
3.0%
Other values (52) 67
67.7%
ValueCountFrequency (%)
4 1
1.0%
5 1
1.0%
13 1
1.0%
15 1
1.0%
21 2
2.0%
23 1
1.0%
25 1
1.0%
35 1
1.0%
37 1
1.0%
39 1
1.0%
ValueCountFrequency (%)
222 1
1.0%
180 1
1.0%
144 1
1.0%
131 2
2.0%
127 1
1.0%
122 1
1.0%
121 1
1.0%
120 1
1.0%
116 1
1.0%
114 1
1.0%

Interactions

2023-12-10T15:43:52.046142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:50.226599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:50.795580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:51.401389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:52.177680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:50.369469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:50.945657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:51.571358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:52.328678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:50.522007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:51.107134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:51.749344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:52.474325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:50.661143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:51.255095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:43:51.886175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:43:57.793779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
111114111.1전체150전라남도나주시74
1111.0000.4891.0000.3950.3950.0000.0000.5050.138
1140.4891.0000.4890.1720.1720.2220.3640.0000.719
111.11.0000.4891.0000.3950.3950.0000.0000.5050.138
전체0.3950.1720.3951.0000.9990.1830.1650.0000.167
10.3950.1720.3950.9991.0000.1830.1650.0000.167
500.0000.2220.0000.1830.1831.0000.6930.0000.000
전라남도0.0000.3640.0000.1650.1650.6931.0000.9730.223
나주시0.5050.0000.5050.0000.0000.0000.9731.0000.000
740.1380.7190.1380.1670.1670.0000.2230.0001.000
2023-12-10T15:43:57.964399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전라남도나주시50전체1
전라남도1.0000.6260.4580.1300.130
나주시0.6261.0000.0000.0000.000
500.4580.0001.0000.2980.298
전체0.1300.0000.2981.0000.977
10.1300.0000.2980.9771.000
2023-12-10T15:43:58.129983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
111114111.174전체150전라남도나주시
1111.0000.4361.0000.4900.2760.2760.0000.0000.242
1140.4361.0000.4360.6410.1960.1960.1300.1630.000
111.11.0000.4361.0000.4900.2760.2760.0000.0000.242
740.4900.6410.4901.0000.1920.1920.0000.0960.000
전체0.2760.1960.2760.1921.0000.9770.2980.1300.000
10.2760.1960.2760.1920.9771.0000.2980.1300.000
500.0000.1300.0000.0000.2980.2981.0000.4580.000
전라남도0.0000.1630.0000.0960.1300.1300.4581.0000.626
나주시0.2420.0000.2420.0000.0000.0000.0000.6261.000

Missing values

2023-12-10T15:43:52.665692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:43:52.951400image/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

MT201901111114111.1가공유전체전체.1150전라남도나주시74
0MT201901889388가공유전체전체150경상북도포항시98
1MT20190111379113가공유전체전체150경상북도경주시64
2MT2019019416394가공유전체전체150경상북도안동시127
3MT201901785178가공유전체전체150경상북도문경시35
4MT201901668966가공유전체전체150경상북도성주군58
5MT20190110188101가공유전체전체150경상남도전체95
6MT20190111392113가공유전체전체150경상남도창원시100
7MT20190110688106가공유전체전체150제주특별자치도전체83
8MT20190111291112가공유전체전체150제주특별자치도제주시86
9MT20190110898108가공유전체전체160전체전체92
MT201901111114111.1가공유전체전체.1150전라남도나주시74
89MT201901349159349가공유딸기우유전체260경기도김포시101
90MT20190112795127가공유딸기우유전체260경기도화성시144
91MT201901105257105가공유딸기우유전체260경기도광주시90
92MT2019013320933가공유딸기우유전체260경기도양주시109
93MT201901202가공유딸기우유전체260경기도포천시5
94MT2019012073802073가공유딸기우유전체260경기도양평군86
95MT201901255114255가공유딸기우유전체260강원도춘천시93
96MT201901149168149가공유딸기우유전체260강원도원주시79
97MT201901-97759-977가공유딸기우유전체260강원도속초시99
98MT201901151715가공유딸기우유전체260강원도철원군21